Deep Siamese Network for Multiple Object Tracking
Bonan Cuan, Khalid Koulali Idrissi, Christonhe Garcia · 2018
Multiple object tracking is an important but challenging computer vision task. Thanks to the significant progress in object detection field, tracking-by-detection becomes a trending paradigm for tracking multiple objects at the same time. Appearance models are also widely used for associating detection results. In this paper, we combine cosine similarity metric learning with very deep convolutional neural network, yielding a robust appearance pairwise matching model: a deep Siamese network capable of re-identifying the same object after a long time and dealing with partial and complete occlusion. Embedded in existing tracking algorithms, our model is a lightweight but powerful module for decision-making among track hypotheses. Experiments on MOT Challenge 2016 benchmark [1] demonstrate the effectiveness of our model, which achieves state-of-the-art performance without delving into extensive hyper-parameter tuning.