Alextrac: Affinity Learning By Exploring Temporal Reinforcement Within Association Chains

This paper presents a self-supervised approach for learning to associate object detections in a video sequence as often required in tracking-by-detection systems. In this paper we focus on learning an affinity model to estimate the data association cost, which can adapt to different situations by exploiting the sequential nature of video data. We also propose a framework for gathering additional training samples at test time with high variation in visual appearance, naturally inherent in large temporal windows. Reinforcing the model with these difficult samples greatly improves the affinity model compared to standard similarity measures such as cosine similarity. We experimentally demonstrate the efficacy of the resulting affinity model on several multiple object tracking (MOT) benchmark sequences. Using the affinity model alone places this approach in the top 25 state-of-the-art trackers with an average rank of 21.3 across 11 test sequences and an overall multiple object tracking accuracy (MOTA) of 17%. This is considerable as our simple approach only uses the appearance of the detected regions in contrast to other techniques with global optimisation or complex motion models.