Rapid octree construction from image sequences
CVGIP: Image Understanding
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian-Competitive Consistent Labeling for People Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-camera people tracking by collaborative particle filters and principal axis-based integration
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multiple camera people detection and tracking using support integration
Pattern Recognition Letters
Multi-view Occlusion Reasoning for Probabilistic Silhouette-Based Dynamic Scene Reconstruction
International Journal of Computer Vision
Multiple Object Tracking Using K-Shortest Paths Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-person localization and track assignment in overlapping camera views
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Tracking multiple people under global appearance constraints
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We present a comparative study for tracking multiple persons using cameras with overlapping views. The evaluated methods consist of two batch mode trackers (Berclaz et al, 2011, Ben-Shitrit et al, 2011) and one recursive tracker (Liem and Gavrila, 2011), which integrate appearance cues and temporal information differently. We also added our own improved version of the recursive tracker. Furthermore, we investigate the effect of the type of background estimation (static vs. adaptive) on tracking performance. Experiments are performed on two novel and challenging multi-person surveillance data sets (indoor, outdoor), made public to facilitate benchmarking. We show that our adaptation of the recursive method outperforms the other stand-alone trackers.