ECCV Workshop on Benchmarking Multi-target Tracking 2016
My paper titled "Online multi-target tracking with strong and weak detections (link)" (R. Sanchez Matilla, F. Poiesi and A. Cavallaro) has been accepted in the European Conference on Computer Vision Workshop (ECCV - Benchmarking Multi-target Tracking: MOTChallenge 2016). We participated to the challenge with a new multi-object tracker based on Probability Hypothesis Density Particle Filter (we named it EAMTT). At submission date EAMTT had the best online tracking results overall in MOT15 and MOT16 among the public trackers. Today (29/08) EAMTT is still ranked first in MOT15 and is ranked second in MOT16. - Abstract - 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. 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 proposed tracker runs on average at 12 frames per second. Results show that our method outperforms alternative online trackers on the Multiple Object Tracking 2016 and 2015 benchmark datasets in terms tracking accuracy, false negatives and speed.