1. Counting In High Density Crowd Videos
Edgar Lopez Haroon Idrees
University of Texas at El Paso University of Central Florida
Motivation
Framework
Results
Minimum NAD
Maximum NAD
We compute SLIC superpixels for each frame using the VLFEAT [4].
The count per pixel is computed using our previous method [1].
Dense trajectories are computed using [2].
The 3-D Conditional Random Field smooths the counts between superpixels in different frames [3].
Crowds occur in a variety of situations:
Political speeches and rallies
Concerts
Marathons
Stadiums
Other important events
Having a crowd count is important for:
Crowd Management
Safety
Analyzing impact of events
Development of public transportation infrastructure
Manual counting is very inefficient and time consuming
NAD: 3.3% AD: 9
GT Average: 278
Estimated Average: 287
NAD: 67.3% AD: 984
GT Average: 1458
Estimated Average: 474
Method λ AD
NAD
CVPR’13
n/a
236.1
30.2
Proposed
1.00
215.6
26.9
0.65
214.2
26.3
0.60
215.8
26.2
0.50
217.7
26.7
Challenges
Superpixels and Dense Trajectories
Perspective
Occlusion
Clutter
Low-Resolution
Counting-by-detection in higher densities not possible
Conclusion
We propose a method for counting by extending a static image crowd count method [1] to videos.
Our method has shown a 4% improvement compared to using the CVPR’13 method alone [1].
Dataset
3-D Conditional Random Field
13 Videos
Average of 162 frames per video
Average of 9 annotated frames per video
Head counts ranging from
Average of 557 head counts per frame
Spatial: We connect all neighboring superpixels so that they have similar counts.
Time: We connect superpixels through dense trajectories, so that they capture flow consistency across time.
References
[1] Haroon Idrees, Imran Saleemi, Cody Seibert, and Mubarak Shah, Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, June 25-27, 2013.
[2] M. Raptis, I. Kokkinos, and S. Soatto. Discovering discriminative action parts from mid-level video representations. In CVPR, 2012.
[3] Zabih, Ramin D., Olga Veksler, and Yuri Boykov. "System and method for fast approximate energy minimization via graph cuts." U.S. Patent No. 6,744, Jun
[4] Vedaldi, Andrea, and Brian Fulkerson. “VLFeat: An open and portable library of computer vision algorithms.” Proceedings of the International conference on Multimedia. ACM, 2010.