![• Histogram is used for the feature extraction.
• The range of the image’s grey level is taken [0,T] where T
=255.
• For difference image d{i,j} the range of its grey scale is
taken between [-T,T] or[0,2T].
• The histogram ha(r) is computed from the image d{i,j}.
• The histogram hb(r) is computed from the edge map E(i,j)
of the image d{I,j}.
• Here r is between –T<=r<=T.
Fig. referred from-wavemetrics.com](https://image.slidesharecdn.com/movingvehicledetection-171128105417/85/Moving-vehicle-detection-from-Videos-9-320.jpg)
![NORMALIZATIO
N
• .Normalisation of the feature is done to adopt the different block division by h(r)=hc(r)/S
where S is the size of block.
• .PCA, optimal orthonormal decomposition will be applied to reduce the noises from
feature.
• .From the collected sample ,a scatter matrix is obtained as.
S= 1/𝑁 𝑖=0
𝑁 ℎ𝑖 − 𝑚 (ℎ𝑖 − 𝑚) 𝑡
,here N =no. of training set.
• hi is the ith vector and (hi − m)t is the transpose of the vector of (hi − m).
• .For S ∈ R(3T +2)×(3T +2), it is easy to obtain its eigenvalues A = diag[λ1,...,λ3T +2], λ1
≥···≥ λ3T +2 and its eigen-vectors V = [ν1,...,ν3T +2]. Then, a new compressed vector
with a dimension p can be computed from the original vector h(r).
• gp = [ν1,...,νp] t h, when p ≤ 3T + 2.](https://image.slidesharecdn.com/movingvehicledetection-171128105417/85/Moving-vehicle-detection-from-Videos-11-320.jpg)
Moving vehicle detection from Videos
- 1. MOVING VEHICLE DETECTION GUIDE PROJECT MEMBERS DR. KHELCHANDRA THONGAM PRIYANK BHARDWAJ ROHIT KUMAR SAHU AJEYA SIDDHARTHA
- 2. INTRODUCTION INTELLIGENT TRANSPORTATION SYSTEM • Traffic data may come from different sensors such as loop detectors, ultrasonic sensors, or cameras. • Video-based camera systems coupled with computer vision techniques offers alternative approach to spot sensors. • VB camera systems are more sophisticated and powerful.
- 3. MOTIVATION • There are many published works for moving vehicle detection but there were certain areas of improvement in each like: • how to detect the moving vehicles at night, casting shadow elimination. • Different from previous works, this algorithm learns from the known examples and does not rely on the prior model of vehicles, lighting, shadows, or headlights as example based classification system is prevalently used in image-based classification.
- 4. CHALLENGES • Spot sensors have limited capabilities and are often both costly and disruptive to install. • Main challenge come from cast shadows, vehicle headlights, and noise which produce incorrect segments. • Sunlight cause shadows which are difficult to be distinguished from the vehicle and cause segmentation errors. • Vehicle headlights and bad illumination cause strong noise.
- 5. ALGORITHM DESCRIPTION The whole algorithm includes the following steps: 1. Background estimation 2. block division 3. candidates’ selection 4. Feature extraction, 5. SVM-based classification, 6. Shape representation. IMAGE SEQUENC E BACKGROUN D ESTIMATION BLOCK DIVISION FEATURE EXTRACTIO N SVM BASED CLASSIFICATI ON SVM- TRAINING SHAPE REPRESENTATI ON CANDIDA TE SELECTIO N Fig. referred from-IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 56, NO. 1, JANUARY 2007
- 6. BACKGROUND ESTIMATION • In order to detect moving vehicles, we need to estimate the background of the scene first. • We will propose improved adaptive background extraction algorithm based on Kalman filtering. • The background on the pixel p of the (n + 1)th frame can be defined as B(n + 1, p) = B(n, p) + β(n, p) + μ(n, p). • The input image intensity is described as I(n, p) = B(n, p) + η(n, p). • By combining (1) with (2), we have I(n + 1, p) = B(n, p) + ω(n + 1, p). Updated area Sliding window Image Fig. referred from-IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 56, NO. 1, JANUARY 2007
- 7. BLOCK DIVISION AND CANDIDATES SELECTION • The image will be divided into non-overlapped blocks, and each block has the same size in a same image. • We will find out the blocks with a gray-level change. • The current image will be subtracted from the background to get the difference image, and we compute its mean value for each block. • These candidates include real vehicles, casting shadows, headlights, or noise.
- 9. • Histogram is used for the feature extraction. • The range of the image’s grey level is taken [0,T] where T =255. • For difference image d{i,j} the range of its grey scale is taken between [-T,T] or[0,2T]. • The histogram ha(r) is computed from the image d{i,j}. • The histogram hb(r) is computed from the edge map E(i,j) of the image d{I,j}. • Here r is between –T<=r<=T. Fig. referred from-wavemetrics.com
- 10. FORMULAS • To compute the edge map,the formula = • E(i, j) = 1/ 2 {|D(i + 1, j + 1) − D(i, j)| + |D(i + 1, j) − D(i, j + 1)|}. • A new histogram hc(r) is from by combining the histogram of the difference image and its edge map. • The new histogram hc(r) of dimension3T+R is computed as • hc(r) = ha(r), 0 ≤ r ≤ 2T hb(r − 2T − 1), 2T + 1 ≤ r ≤ 3T + 2.
- 11. NORMALIZATIO N • .Normalisation of the feature is done to adopt the different block division by h(r)=hc(r)/S where S is the size of block. • .PCA, optimal orthonormal decomposition will be applied to reduce the noises from feature. • .From the collected sample ,a scatter matrix is obtained as. S= 1/𝑁 𝑖=0 𝑁 ℎ𝑖 − 𝑚 (ℎ𝑖 − 𝑚) 𝑡 ,here N =no. of training set. • hi is the ith vector and (hi − m)t is the transpose of the vector of (hi − m). • .For S ∈ R(3T +2)×(3T +2), it is easy to obtain its eigenvalues A = diag[λ1,...,λ3T +2], λ1 ≥···≥ λ3T +2 and its eigen-vectors V = [ν1,...,ν3T +2]. Then, a new compressed vector with a dimension p can be computed from the original vector h(r). • gp = [ν1,...,νp] t h, when p ≤ 3T + 2.
- 12. EXTRACTED FEATURE • Using compressed vectors as features, our experiments will show that the classification result is better than directly using the original histograms. Moreover, the computation cost can also be reduced. Fig. referred from- images.google.com
- 13. 4.SUPPORT VECTOR MACHINE BASED CLASSIFICATION.
- 14. SVM • Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. • It is used to generalization capabilities which are achieved through a minimization of bound of the general error. • The aim is to define a hyperplane that divide the set of examples such that all points with the same bound are on the same side of the hyperplane. • The extracted features are put into the SVMs for training and the parameter of the SVM-based classifier can be obtained. Fig. referred from- wikipedia.org
- 15. DETECTION STAGE • The input is divided into many blocks. • The feature vector is generated by PCA from the two kinds of histogram of the images. • It is put into the well-trained classifier to judge whether this region is covered with a vehicle or others (such as shadow, headlight, or noise).
- 17. SHAPE OF VECHICLES NEEDS TO BE EXTRACTED FOR VEHICLE COUNTING,SPEED ESTIMATION,TRACKING ETC. SVM BASED CLASSIFIER GROUPS BLOCKS INTO VEHICLES AND NON VECHICLES BLOCKS CLASSIFIED AS VEHICLES ARE GROUPED BY THEIR 8 CONNECTIVITY A CONVEX POLYGON IS DERIVED FROM A SET OF RELATED BLOCKS THE POLYGON IS USED TO APPROXIMATE A VECHICLE’S SHAPE IN THE FORM OF A PARALLELOGRAM WHICH IS MORE ROBUST DUE TO ITS SIMPLE REPRESENTATION Fig: 8 direction connectivity Fig:Parallelogram P1P2P3P4Fig. referred from-IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 56, NO. 1, JANUARY 2007
- 18. Fig: White Boxes indicate detected parts of vehicles Fig: Shape representation Fig. referred from-IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 56, NO. 1, JANUARY
- 19. TECH REQUIREMENTS MATLAB LATEX MICROSOFT POWERPOINT
- 20. PROJECT’S TIME DIVISION S.No. Description Date 1. Research 10/10/17 – 11/11/17 2. Project Implementation 15/11/17 – 31/01/18 3. Documentation 01/02/18 – 25/02/18
- 21. FUTURE WORK THE ALGORITHM CAN BE MADE MORE PRECISE BY USING MORE INFORMATION SUCH AS COLOR USING SOME OTHER CLASSIFIERS SUCH AS CNN OR A COMBINATION OF CLASSIFIERS TO IMPROVE ACCURACY
- 22. REFERENCES JIE ZHOU , DASHAN GAO AND DAVID ZHANG , “MOVING VEHICLE DETECTION FOR AUTOMATIC VEHICLE MONITORING,” IEEE TRANSACTION ON VEHICULAR TECHNOLOGY , VOL 56 , NO. 1 , PP. 51-59, JAN 2007 ALI SHARIF RAZAVIAN ,HOSSEIN AZIZPOUR, JOSEPHINE SULLIVAN, STEFAN CARLSSON, “CNN FEATURES OFF- THE-SHELF: AN ASTOUNDING BASELINE FOR RECOGNITION”, 2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS
- 23. THANK YOU