One shot scene specific crowd counting
- 1. One Shot Scene Specific Crowd Counting Mohammad Asiful Hossain, Mahesh Kumar K, Mehrdad Hosseinzadeh, Omit Chanda and Yang Wang Presented by Hafsa Moontari Ali ID: 7880835 Course Title: Research Methodology Winter 2020, University of Manitoba
- 2. What is Crowd Counting? ●A technique used to count or estimate the number of people in a crowd. ●The most solution to crowd counting is to actually count the number of people from a crowd. ●But it becomes difficult when the images of crowd are captured from open areas such as streets or parks. 2
- 4. The problem becomes harder... 4
- 5. Where are the potential application areas? ●Urban planning ●Surveillance ●Traffic monitoring ●Geo-political analysis 5
- 6. Contribution of this paper ●Addressed a novel problem, “one-shot scene-specific crowd counting”. ●Generated a crowd counting model using Deep Learning. ●Significantly outperformed baseline methods. 6
- 7. Proposed Approach ●A density map is predicted from the input static image. ●Each pixel of the density map indicates the crowd density at the corresponding location in the image. ● ●Crowd counting is obtained by summing the entries of the density map. 7
- 8. Model Architecture ●Dilated Convolutional Neural Networks(CSRNet) architecture is used as backbone. ●It employs convolutional neural network to extract features. ●Dilated convolutional neural network generates output from the features. ●The split of encoder/decoder is flexible and application specific. 8
- 9. Model Architecture Figure 1: One-shot scene-specific adaptation using CSRNet 9
- 10. Model Learning ●During training, a collection of labeled training images are used. ●Each scene might correspond to a camera fixed at a particular location. ●It is assumed that each scene has same number of N training images. ●The model can be generalized where different scenes have different number of training images. ●During training, the model learns the parameters of the encoder network. 10
- 11. One Shot Scene Specific Adaptation ●During testing, the crowd counting algorithm is deployed in a specific target scene. ●In this paper, one-shot learning is applied by fine tuning the decoder network. ●The distance between predicted density map and ground truth density map is considered as loss function. ●Fine tuning is done by computing the gradient of the distance. ●The model is effectively tuned to the target scene. 11
- 12. Experimental Results T Table 1: Comparison of the performance (MAE and MSE) of our approach and the baselines on the WorldExpo’10 dataset and Trancos dataset. For “ours” and “simple fine-tuning”, either using the last layer or the last two layers of CSRNet as the decoder are considered. 12
- 13. Cross Dataset Testing Table 2: Performance in the cross-dataset testing with the same(a,b) and different (c,d) object. “W”, “U”, “M” and “T” are used to denote WorldExpo’10, UCSD, Mall, Trancos, respectively. 13
- 14. Future Work ●This paper attempts to deploy a crowd counting model in real-world application. ●In future, this approach can be extended to few shot learning. ●Meta learning, meta-auxiliary learning can also be employed. ●This approach can be extended for unsupervised learning. 14
- 15. 15 Any Question?