Enhancing human detection using crowd density measures and an adaptive correction filter

In this paper we present a method of improving a human detector by means of crowd density information. Human detection is especially challenging in crowded scenes which makes it important to introduce additional knowledge into the detection process. We compute crowd density maps in order to estimate the spatial distribution of people in the scene and show how it is possible to enhance the detection results of a state-of-the-art human detector by this information. The proposed method applies a self-adaptive, dynamic parametrization and as an additional contribution uses scene-adaptive learning of the human aspect ratio in order to reduce false positive detections in crowded areas. We evaluate our method on videos from different datasets and demonstrate how our system achieves better results than the baseline algorithm.

[1]  Thomas Sikora,et al.  Real-time person counting by propagating networks flows , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[2]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Ivan Laptev,et al.  Density-aware person detection and tracking in crowds , 2011, ICCV.

[4]  Florian Schmidt,et al.  Integrating pedestrian simulation, tracking and event detection for crowd analysis , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[5]  Mubarak Shah,et al.  A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Rubén Heras Evangelio,et al.  Complementary background models for the detection of static and moving objects in crowded environments , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[7]  Takeo Kanade,et al.  Tracking in unstructured crowded scenes , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  G. Rigoll,et al.  Unified hierarchical multi-object tracking using global data association , 2013, 2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS).

[9]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Rainer Stiefelhagen,et al.  The CLEAR 2006 Evaluation , 2006, CLEAR.

[11]  Tobias Senst,et al.  Robust Local Optical Flow for Feature Tracking , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  J. Ferryman,et al.  PETS2009: Dataset and challenge , 2009, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance.

[13]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Grantham Pang,et al.  People Counting and Human Detection in a Challenging Situation , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Volker Eiselein,et al.  Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[17]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[18]  Ivan Laptev,et al.  Data-driven crowd analysis in videos , 2011, ICCV.

[19]  Mubarak Shah,et al.  Floor Fields for Tracking in High Density Crowd Scenes , 2008, ECCV.

[20]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[21]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.