We propose an online multi-target tracker that exploits both high-
and low-confidence target detections in a Probability Hypothesis Density Particle
Filter framework. High-confidence (strong) detections are used for label
propagation and target initialization. Low-confidence (weak) detections only
support the propagation of labels, i.e. tracking existing targets, when strong
detections are not available. Moreover, we perform data association just after
the prediction stage thus avoiding the need for computationally expensive
labelling procedures such as clustering. Finally, we perform sampling by
considering the perspective distortion in the target observations. The tracker
runs on average at 12 frames per second. Results show that our method
outperforms alternative on-line trackers on the Multiple Object Tracking 2016
and 2015 benchmark datasets in terms tracking accuracy, false negatives and speed.
Online multi-target
tracking with strong and weak detections pdf
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro
Proc. of 2nd Workshop on Benchmarking Multi-target Tracking: MOTChallenge 2016, Amsterdam, October 9, 2016
Videos