A review of statistical data association for motion correspondence
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
International Journal of Computer Vision
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Cascaded confidence filtering for improved tracking-by-detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
On collaborative people detection and tracking in complex scenarios
Image and Vision Computing
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In this paper, we propose a two-phase tracking algorithm for multi-target tracking in crowded scenes. The first phase extracts an overcomplete set of tracklets as potential fragments of true object tracks by considering the local temporal context of dense detection-scores. The second phase employs a Bayesian formulation to find the most probable set of tracks in a range of frames. A major difference to previous algorithms is that tracklet confidences are not directly used during track generation in the second phase. This decreases the influence of those effects, which are difficult to model during detection (e.g. occlusions, bad illumination), in the track generation. Instead, the algorithm starts with a detection-confidence model derived from a trained detector. Then, tracking-by-detection (TBD) is applied on the confidence volume over several frames to generate tracklets which are considered as enhanced detections. As our experiments show, detection performance of the tracklet detections significantly outperforms the raw detections. The second phase of the algorithm employs a new multi-frame Bayesian formulation that estimates the number of tracks as well as their location with an MCMC process. Experimental results indicate that our approach outperforms the state-of-the-art in crowded scenes.