Activity Recognition Using RGB-Depth Sensors-Final report

[PROJECT TITLE] 1
Activity Recognition Using RGB-Depth Sensors
Issue date: 03.03.2016
Student(s):
• Nazli Temur
Supervisor(s)
• Francois Bremond
• Carlos Fernando Crispim Junior
Projet étudiant M1
2015-2016 [ R |
Final Report

Abstract
Activity Recognition deals with the representation of the real activities in a way that they can
be recognized by machines. In other words once modeling of human vision was achieved,
artificial objects [cameras] that are capable of capturing and replicating view was
introduced. This example can be generalized for modeling the activity portion of the reality.
Once event modeling is achieved, the assumption is to come up realtime - real world event
recognition through artificial objects.
Current state of art contains several methods to achieve intelligent tracking. Some methods
are machine learning oriented.In these methods , activities are learnt from the context in an
unsupervised or semi supervised manner. One other methods is description based event
recognition. In the heart of the method , describing scenarios wrt activities employed. For the
description, a language is necessarily needed. There are mathematical languages in which
logic is used to represent activities and their relations.Also some graphical languages such as
hidden markov models, state machines, state charts are being used. Some textual languages
proposed aswell.
In this study , i will be performing an analysis on a constraint satisfaction applied, RGBD
based algorithm to improve the algorithm. I dealt with unconstraint environment of a nursing
home. Existing algorithm includes detection-tracking and recognition. To be able to improve
the recognition, i dealt with its description based nature by investigating defined event models
and constraints. I find it useful to extend existing event scopes with modeling existential
qualifications, a route-target, appear, disappear and increase and decrease properties.
Algorithm improvement is done with respect to recognition of multiple actor scene.
Evaluation of the newly proposed event models are done after evaluating existing
performance of the algorithm. Currently i am not able to claim a strong improvement in the
algorithm because of a computational question of the event recognition. In this study i
showcased the challenges that i overcome and learnings.
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[PROJECT TITLE]

Table of Contents
1.General Project Description 4..........................................................
Context 4.....................................................................................................................................................
Motivations 6...............................................................................................................................................
Challenges 6................................................................................................................................................
Goals 7.........................................................................................................................................................
2.State of the Art 8..........................................................................
3.Work done so far : progresses achieved during the period March-May 9......
4.Work Progress of Second Half 19......................................................
5.Conclusion 25..............................................................................
6.Bibliography 26............................................................................
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1. General Project Description
Context
Domain of the project is automatic sequence video interpretation. The “SUP” (“Scene
Understanding Platform”) Platform developed in STARS, detects mobile objects, tracks their
trajectory and recognizes related behaviors predefined by experts.
This platform contains several techniques for the detection of people and for the recognition
of human postures and gestures of one person using conventional cameras. Classification into
more than 8 classes (e.g Person, Group, Train) based on 2D and 3D descriptors (position,3D
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[PROJECT TITLE]
Generic Platform for Activity Understanding

ratio height/width,..) can be seen below.
Example of 4 classes : Person, Group, Noise, Unknown.
People detection using local descriptors;
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Moreover RGBD sensors have been released improving people detection. This kind of sensor
is well adapted for applications which monitor persons (e.g. monitoring Alzheimer patient in
hospital): because the people are in a predefined area and near the camera. The depth cameras
have two main advantages: first, the output images contain depth information and second, the
sensor is independent on the light changes (IR sensor). In this work, we propose to use RGBD
sensor to acquire 3D images, detect people and recognize interesting activities. The SUP
library is able to compute some treatments (e.g. detection of the people) and to provide a true
3D map of the scene in the referential of the RGB-D sensor.
Motivations
This Project Course consists in the improvement of the activity recognition process using
RGB-Depth sensors to improve monitoring systems for older adults.
Many techniques have already been proposed for detecting people in specific environment
(e.g. low level of occlusion) on short time duration (e.g. few minutes). However, activity
recognition is still brittle on long term (e.g. few weeks) and often depends on the scene
conditions (e.g. number of people, occlusion, and people interactions).
This work aims at reducing these hypotheses in order to conceive a general algorithm
enabling the recognition of people activities on long terms living in an unconstrained
environment and observed up to a limited number of cameras (e.g. 2 RGB-D sensors). The
goal is to review the literature, evaluate existing libraries and propose more robust algorithms
and more accurate activity models.
To validate the Project Course, proposed approach on will be assessed via homecare videos
from Nice Ehpad to evaluate technologies to keep older adults functioning at higher levels
and living independently.
Challenges
There are scientific challenges in people detection when dealing with real word scenes with
apathetic patients: cluttered scenes, handling wrong and incomplete person segmentation,
handling static and dynamic occlusions, low contrasted objects, moving contextual objects
(e.g. chairs)…
Moreover, with the connection of people detection and tracking, recognizing the behavior of
the person still remains a challenge. Event modeling is a way to structure the behavior
recognizable. Indeterministic property of real world , naturally contributes to this challenge by
increasing complexity of the event nature.
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[PROJECT TITLE]

Briefly, automatic recognition of the events which involves complex events are challenging.
Creating a real time algorithm that achieves complex event recognition is strongly desired by
using RGB Dept camera and multi sensor combination is a way to improve.
Goals
Main purpose of the project is to improve existing recognition algorithm and its application
level solution. To achieve this, firstly recognition performance of the existing algorithm
should be evaluated. Afterwards, possible problems that impact algorithm efficiency
negatively is needed to identified. Moreover, with respect to identified problems,
improvements are necessary to be proposed as a part of their applicability in a restricted time
and their feasibility.
Briefly, the goals are;
1st month:
● Study the limitations of existing solutions and improve the current tool to visualize the
recognized events.
2nd month:
● Proposing more accurate activity models.
3rd month:
● Evaluate and improve the current algorithm for recognizing activities.
4th month:
● Optimize the proposed algorithm for recognizing activities and write report.
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2. State of the Art
Video surveillance systems are automatically recognizes complex events involving several
actors. This recognition process are named in literature as video understanding ,video activity
recognition, event recognition or scene understanding
Video understanding is one of the most trending topic of last decade. Many recent
approaches have been pro-posed. Any happening that can be tracked via video cameras is
in the scope of video understanding. Recently, composed sensory information became
popular to support video understanding along with video cameras.
Briefly, video understanding is about making intelligent observations from video in
an offline or online manner. To achieve this , it is necessary to gather scene in a specific
wrt specific details. Recently , RGB-Dept cameras became popular because they are
advantageous for capturing scene with 3D information.
Some methods contains necessary wearable to be carried all along recognition
process,some not.
Howell and Buxon proposed an approach to recognize a video event based on a neural
network. But this method remained inefficient to recognize complex events. Complex events
are basically the events are combination of multiple events wrt number of actor, time and
other constraints such as simultaneous occurrence or re occurrence.[4]
Geber defined a method to recognize a video event based on fuzzy temporal logic.
Pinhanez and Bobick have used Allen’s interval algebra to represent video events.
Shet have recognized activities based on Prolog Rules.
Rota and Thonnat used constraint resolution technique to recognize video events.
Another approach is using a symbolic network which stores partially recognized video
events.
A chronicle is a set of temporal constraints on time stamped events and Ghallab used this
chronicle terminology to express video event.
Vu have combined previous approaches to optimize the temporal constraint resolution by
ordering video events to be recognized in time .[4]
Lavee et al. categorizes computer vision approaches for event recognition in following
categories;
State Models,
Pattern Recognition
Semantic Models,
Multi sensor approaches for event recognition generally perform fusion at input data or
feature level using State Models or Pattern Recognition Approaches. But those methods are
generally complex enough to be applied real life scenarios because of their semantic
weaknesses.[2]
To cope with this difficulty , in the existing Event Recognition Algorithm, by Vu, Hierarchical
Model Based Framework. In other words, a generic onthology is described by the help of
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[PROJECT TITLE]

multi sensor data as Event Models. Basically framework is composed of two main
components a generic ontology which includes temporal event algorithm that is inspired from
Ghallab. Algorithm takes the pre-defined event models wrt their temporal priority and returns
whether constraints are satisfied or not.[3]
3. Work done so far : progresses achieved during the
period March-May
Briefly, proposal of a new algorithm which will contribute to the recognition improvement of
the existing algorithm is done. The current progress is achieved at four steps.
Firstly, reference documents and application of algorithm the existing algorithm is
investigated.
Secondly, source videos and their ground truths’ are verified that they are complete enough to
start for algorithm performance evaluation. For that necessary installations are done.
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Thirdly, algorithm is evaluated .
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For the evaluation, TP,FP,FN measures are taken into account to have some idea about
accuracy,precision, F-Measure of the Algorithm.
Lastly, wrt evaluation results, event models are investigated and findings are discussed with
the supervisor. Then, implementation of a demo is started with current size of 1.5KLOC.
Those 4 steps were the basis of proposed methods of improvement.
Recently, I have identified that existing event models are defined to work with single actor
instead of multiple actor. So , as an improvement multiple actor involving recognition is
targeted . So that, there is an existing need for new event model definition. This new model
definition involves the “benefiting from existential qualifications” and Thinth language syntax
will be extended for this purpose. We are aiming to newly define increase and decrease
events that are triggered via newly defined, appear and disappear events. I formed a
reachability graph in which multiple actors can move through certain zones.Similarly, certain
zones are called as IO zone where people can firstly appear or disappear. To be able to define
event models, we have to follow Thinth Language rules syntactically, then we will be testing
strength of event models with HomeCare Videos.
The other improvement for the multiple actor based scene is to benefit from trajectories of
each actors. The idea comes with the investigation of existing Event Models. Those are max 2
second defined , short durational events. so that their occurrence does not have a predictable
sign for the occurrence of semantic coherency of the events. If we can somehow anticipate a
coherency in between events or occurrence of a certain event, we can achieve stronger
decisions to prevent noise and misconception. To achieve , instead of retaining “Enter”,”Exit”
Events we are going to model approaching events and with the help of past trajectories of
each actor, we are going to distinguish set of activities wrt actor to actor.
The basic idea is about modeling a route-target relation. So that as we have in real life, the
person who follows the route reaches to the target. In other words, a route will become a
constraint to satisfy occurrence of the event that is “reaching to the target”. Primarily, we
observed that some of the recognition problem arises from either misclassified positioning
(direction) of the person or constant noise presence of which invalidates single actor
scenarios.
To be able to understand how this route-target idea can help to the improvement of the
recognition, i started to implement a demo in which 3d point positions of the each
actor(detection algorithm outputs an xml) is parsed. By the help of 3d point coordinates i am
extracting the trajectories of each actor automatically. Then, localizing the actor i estimate the
direction and of the actor via triangulating 3d temporal point positions of the actors.
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After obtain the direction.Then i apply a strong matching technique, via growing circles from
the 3d point coordinates. The details of that algorithm is as below and for now , we are able to
validate or invalidate that trajectory of a person matches with a certain route.
For now i concentrated on the ENTER_RESTROOM event. Furthermore possible route will
be extracted as a sign of event signature.
While doing trajectory analysis , the strategy was to retrieve last 10 points [t/t-10] from
trajectory . We performed an analysis on the sample data , to be able to observe a pattern .
The details of the algorithm is as below. Afterwards i will try to come an integer value which
represents certain path. For now , i am evaluating the angle as a trajectory of a curve. If not, i
will be improving my proposal.
Proposed Route Algorithm:
By the help of 3D position coordinates of person, trying to extract the trajectory of the person
and estimate whether his target is Restroom or not.
This will help us to generate an”Person is approaching” event . Realization of the
ENTER_RESTROOM can be strengthen with the help of trajectory.
We are reading 3d coordinates of person from a file and writing into “vect_list” linkedlist.
Then we are computing distance in between every three sequential points.
Lets assume below are our 3d point set.
Step1: obtain points;
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Step2 : Compute distances wrt triangulations, So that by the help of Cosinus Theorem, we can
obtain
the angle of second point which preserves the rotation amount.
//Any new point generates a triangle with previous two point.
Basically if the angle is 180 , this means person goes straight. Otherwise, person draws a
curve.
If the centroid is equal to p2, the trajectory of 3 points is a straight line.
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Also based on the centroid position change direction we can understand the global
directioning, in other words , even though there are locally left right positioning(jagging),
global positioning could be through left direction.
To locate the movement, I introduced 4 direction(Target Position). Because left and right is
not generic,
it is a reference to state as a part of previous position what direction is the next positioning. To
be able
to use this reference, be should define the current direction(positioning).
I did not take into account the y coordinate , because object length is assumed to be same
while position changes.
D1 is defined by the help if two
sequential point, if wrt to p1 point, z
and x coordinate of p2 increases,
person goes throuh D1,
D2 is defined by the help if two
sequential point at time (t0,t1); if wrt
to p1 point, z and x coordinate of
p2 decreases, person goes throuh D4,
and so on.
The amount of increase and decrease
is not taken into account.
LEFT and RIGHT is determined by previous positioning , so any coordinate change which
follows a
sub sequence of D2==>D1==>D3==>D4 is going through right side,
and the reverse is the left side.
But get to know about the direction is not enough to determine target, so that we will benefit
from the
gradient of the theta.
I did two computation wrt each angle at the trajectory.
One of them is the computation of the average theta wrt time instance, through sum of all
angles
cumulatively and averaging,
p1+p2/2
p1+p2+p3/3
p1+p2+p3+p4/4
but this is not really informative. What we can observe is whether person goes through a line
(average
of all occurred positionning has 179-180) otherwise it descreses.
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Then I computed the gradient of the angle wrt each two point (alternative way is to compute
the
gradient of the angles wrt centroids, which preserves 3 point angles).
theta2-theta1
theta3-theta2
theta(n)-theta(n-1)
this is informative, because whenever we have a value below 10, we have a regular curve , we
obtain a value in between 90 to 30 , the person have a strict curve (Turn).
To be able to understand how useful is the gradient of the theta, we obtained a sample set of
ENTER_RESTROOM event trajectory and tried to compared gradients of with base path wrt
last 10 points. But it is clear that we need more points to reach a conclusion.
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[PROJECT TITLE]

While doing trajectory analysis , we investigated last 10 points [t/t-10] from trajectory . By
the help of gradient of the angle or cumulative average of the angle , we tried to find a
signature integer wrt ENTER_RESTROOM event. We performed an analysis on the sample
data , to be able to observe a pattern . Unfortunately, we could not able to reach a conclusion
on the trajectory analysis.
Additionally, I have the idea to grow circles around each 3d point of the trajectory and obtain
a possible base path. Radius can be set by two point distance, so that, wrt speed , the set of
points that we will work with can be estimated.
The problem is to determine whether trajectory of an actor follows the base-path. A path can
be basically represented as a 2d polygon of 3d point correspondences or curvatures. So in the
algorithm, we are generating a list L1 to store base path-which is a collection of 3d point
coordinates. Then we are growing the points as a form of circles. To define the circle, we can
compute a radius by taking into account 2 consequent point distance, {r} and assuming the
center of the circle,{c} id the 3d point coordinate. Any point set which is equally distant from
center c with radius r is namely in the AREA, which validates the path. Additionally , the
distance in between two points preserves the speed of the actor. In the case we have greater
radius, it is obvious that we will obtain the less number of points compare to base path. If the
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point is not in the Range of AREA, we are going to traverse through the base path to find out
the closest point of the base path to the external point(test point).This will let us to measure
the point distance to the base path and if the direction of the each point is at the side of path ,
with the help of constant velocity , we can estimate whether , possible future position is in the
path or not. This method is useful because, each point set is taken into account with respect to
past trajectory of its single actor. The advantageous in the case there are multiple actors
present at the scene, it is not possible to confuse with other actor's trajectories.
Code of the project and other reports are currently available in case of request by reminding
that to be updated.
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[PROJECT TITLE]

4. Work Progress of Second Half
For the second half of the project, the main goal was to complete the implementations of the
both event models and trajectory solution to showcase the improvements. If there is not
enough improvement , addressing the problematic parts by cause analysis is decided.
Java implementation of the solution is almost done, to be able to test the approach before
integrating the solution to the SUP platform. But still we had the missing determination about
how to benefit from angle of the point positions wrt trajectory. Defining a curve and fitting a
curve is not generic enough to apply. Even though in the java application, while parsing the
tracking output xml file, we were able to consolidate the information as per each defined
actor, in the case detection is lost and person is occluded for a while , we were missing some
details of the information. So that we have we did not implement complete trajectory proposal
into sup. we only implemented the c++ code in which we triangulate the points and compute
the angles of the each point position. Then, with respect to gradient of the points , a flag is
returned to the event models, which informs person moves with a constant angle or person
performs slight turns via increase and decrease of the angles.
This slight movement information could have been combined with the fact that if the person
moves with a constant angle of a strait line, by the help of CHANGE_ZONE_TO_X_from_K
primitive events, we could have eliminate some patterns such as enter restroom. But still the
reasoning is not complete and there is a serious need of reviewing state of art of trajectory
related solutions.
After, i started to focus on event modeling for improvement of activity recognition.
In my project activity recognition method is description based activity recognition. In other
words, modeling real world activities to recognize exactly what you have modeled. The
advantage of this models is that it allows you to easily control the recognition scope. Very
easy to define very complex event models which include parallelism, dependency on some
strict constraints, timely order , multiple actors and so on. In the existing method , the
definition of activities is done in a modular manner which means activities are composable .
The method includes Allen’s interval. Allen interval can be thought of an interval graph in
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which adjacent (touching) lines are representation of a time window from past to present. So
that chronicles can be used including after , before , just before, just after , meet and so .
Additionally event hierarchy is defined in which Composite Events are the composition of
Primitive Events or Primitive States. While modeling events , it is necessary to define
maximum of 2 components to be validated.
Previously defined activity models are as below. As a part of my observation, previously
defined event models are not suitable for a multiple actor presence in the scene. Existence of
multiple actor invalidates the to be recognized event model.
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[PROJECT TITLE]

To be able to expand the expressivity and functionality of existing event recognition
algorithm , i have proposed to use number of mobile actor change as a signature of appear
and disappear.
So that while doing recognition in an occluded scene, we can address the invisibility of a
person with respect to true event , such as ENTER_RESTROOM or
EXIT_AREA_IN_BEDROOM. It is same for the visibility / appear-reappear.
One of the Newly defined event model is as below.
For defining an exit restroom there can be another approach.It is binding Exist Restroom to
Enter Restroom event, so that we could distinguish an appear from a re-appear. But currently,
algorithm does not perform well for the recognition of ENTER_RESTROOM, so when ever
we miss an ENTER_RESTROOM event, we will be missing an EXIT_RESTROOM since
they are coupled or binded. So that we have a suggestion for the users of the real time
systems/reactive systems. The description language of the Event Recognition is necessarily
needs to support modular definition of the activities.
After ensuring each module of the event performs well, then they can be associated to the
more complex events/event sets. What i have defined is modular.In other words, composite
events of ENTER_EXIT does not have dependancy.
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CompositeEvent(ENTER_RESTROOM,
PhysicalObjects((p1 : Person), (z1 : Zone), (sc : Scene))
Components((c1: CompositeEvent Disappear(p1,sc))
(c2: PrimitiveEvent IN_AREA_IN_RESTROOM(p1,z1)))
Constraints((c2 justBefore c1)
(duration(c2) > 0.5))
Alarm ((Level : URGENT))
)
CompositeEvent(Disappear,
PhysicalObjects((p1 : Person), (sc : Scene))
Components((c1: PrimitiveState Person_exists(p1))
(c2: PrimitiveState Negative_Change_Actor_Number(sc)))
Constraints(
(c1 justBefore c2)
(!(Exist(p1))))
)
PrimitiveState(Negative_Change_Actor_Number,
PhysicalObjects((sc : Scene))
Constraints ((AttributeChange(sc->NumberOfActors) = 2))
)

Additionally, I have defined Appear/Re-Appear/ChangeZONE_X_to_ZONE_y/
ENTER_R_Fast events, to be able to observe some optimal properties such as what can be the
optimal duration to be able to recognize events wrt quick and slow actors without missing.
But evaluation of each change in the event models take average of 5 hours to output
evaluation result. So that I have concentrated on the verification of multiple actor scene via
ENTER_RESTROOM and EXIT RESTROOM.
For the evaluation i have used 2 kinds of metrics,
-with respect to Frame Number 4.3
-wrt Event 4.1
after manually validating the metric performance we came up with the fact that , 4.3 is much
stronger to represent real output performance.
As a part of first evaluation, we could not able to get any result from 4.3 metric for the
updated event models,
so that we performed an analysis wrt 4.1 metric and results are as below.
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[PROJECT TITLE]

In the table, First result belongs to existing event models/ base case to compare
improvements.
The STEP1 includes new models and new zones,
The STEP2 includes new extended models and same zones as STEP1.
The STEP3 includes models of STEP2 with new update on Zone Definitions.
As you can see from the table, we got rid of almost all false positive events, by the help of
new models. TP number is decreased by 2 and when we check manually, we have seen that
the main problem is the decrease of zone area. Since Ground Truth annotation is done wrt
previous zones, in the case we decrease the zone , we had to update the ground truth file.
So that we have defined new zones wide enough and did a second evaluation.STEP3
STEP1 and STEP2 has the different event models. After defining event model of ENTER/
EXIT RESTROOMS we observed that there are the cases, Instead of Enter_Restroom , EXIT
RESTROOM is recognized. After investigation, we found out that disappear event occurrence
is sometimes triggered later than IN_ZONE_RESTROOM, and sometimes triggered before
than IN_ZONE_RESTROOM. There is a need of investigation with respect to people
tracking, to have a clear answer about these output.
But to remind that 4.1 metric does not really represents the reality, we approached to use 4.3
Frame Metric instead. This metric is very sensitive. Each second composed of on average 6-7
frames, and evaluation is done wrt exact frame numbers. which means we are considering
millisecond matching.
Below you can see the results wrt. 4.3 Frame Metric Execution. Because of a bug at the
VISEVAL Tool, we could not able to output any value wrt 4.3 for the new evaluation.
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After fixing the bug, we have repeated the evaluations. Result is as below.
After getting this result we have investigated possible cause behind the fact . What we
observe is that Exit_Restroom is called each time ENTER_RESTROOM occured. Then when
we compare the event models we have seen that, the recognition of disappear event
sometimes occurs before IN_ZONE constraint and sometime occurs after.
To overcome this , event duration constraints can be experimented for an optimal setting. Also
tracking algorithm can be checked to figure out whether re-identification causes this problem
or not.
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[PROJECT TITLE]

5. Conclusion
In this project, description based video activity recognition and evaluation is studied.
Improvement proposals are suggested on Event Modeling and Trajectory Analysis Topics.
Thanks to the newly proposed event models, it is possible to recognize events in a scene
where multiple actors and noises are present.
For the evaluation process, we benefited from several tool such as SUP Event Recognition
Platform,Viseval ,Viper, KreateTool.
While evaluating , we have used True Positive,False Positive and False Negative
measurements; F1 score, precision, recall metrics.[TP,FP,FN]
Evaluation on previously defined event models are completed.
Currently, latest assessment on newly defined zones and event models is not resulted with a
significant improvement. Necessary analysis is done and there is a need of investigation to
comment why significant improvement is not done. As a result of newly defines event
models, we can comment that, at least for the ENTER_RESTROOM event, newly defined
models are sufficient to replace with EMTY_SCENE to overcome the single actor scene
problem.
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6. Bibliography
[1] C. Crispim-Junior, K. Avgerinakis, V. Buso, G. Meditskos, A. Briassouli, J.
Benois-Pineau, Y. Kompatsiaris and F. Bremond. Semantic Event Fusion of Different
Visual Modality Concepts for Activity Recognition, Transactions on Pattern Analysis
and Machine Intelligence - PAMI to appear, 2016.
[2] C. Crispim-Junior, V. Bathrinarayanan, B. Fosty, A. Konig, R. Romdhane, M.
Thonnat and F. Bremond. Evaluation of a Monitoring System for Event Recognition
of Older People. In the 10th IEEE International Conference on Advanced Video and
Signal-Based Surveillance 2013, AVSS 2013, Krakow, Poland on August 27-30, 2013.
[3] C. Crispim-Junior, B. Fosty, R. Romdhane, F. Bremond and M. Thonnat.
Combining Multiple Sensors for Event Recognition of Older People. In the 1st ACM
International Workshop on Multimedia Indexing and Information Retrieval for
Healthcare, MIIRH 2013, Copyright 2013 ACM 978-1-4503-2398-7/13/10, http://
dx.doi.org/10.1145/2505323.2505329, Barcelona, October 22, 2013.
[4] Alberto Avanzi, Francois Bremond, Christophe Tornieri and Monique Thonnat,
Design and Assessment of an Intelligent Activity Monitoring Platform, in EURASIP
Journal on Applied Signal Processing, special issue in "Advances in Intelligent Vision
Systems: Methods and Applications", 2005.
[5] E. Corvee and F. Bremond. Haar like and LBP based features for face, head and
people detection in video sequences. In the International Workshop on Behaviour
Analysis, Behave 2011, Sophia Antipolis, France on the 23rd of September 2011.
[6] C. Crispim-Junior and F. Bremond. Uncertainty Modeling Framework for
Constraint-based Elementary Scenario Detection in Vision System. In the First
International Workshop on Computer vision + ONTology Applied Cross-disciplinary
Technologies in conjunction with ECCV 2014, CONTACT-2014, Zurich, Switzerland,
September 7th, 2014.
[7] A. König, C. Crispim, A. Covella, F. Bremond, A. Derreumaux, G. Bensadoum, R.
David, F. Verhey, P. Aalten and P.H. Robert. Ecological Assessment of Autonomy in
Instrumental Activities of Daily Living in Dementia Patients by the means of an
Automatic Video Monitoring System, Frontiers in Aging Neuroscience - open access
publication - http://dx.doi.org/10.3389/fnagi.2015.00098, 02 June 2015
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[PROJECT TITLE]

Activity Recognition Using RGB-Depth Sensors-Final report

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