Online multi-object tracking with convolutional neural networks

In this paper, we propose a novel online multi-object tracking (MOT) framework, which exploits features from multiple convolutional layers. In particular, we use the top layer to formulate a category-level classifier and use a lower layer to identify instances from one category under the intuition that lower layers contain much more details. To avoid the computational cost caused by online fine-tuning, we train our appearance model with an offline learning strategy using the historical appearance reserved for each object. We evaluate the proposed tracking framework on a popular MOT benchmark to demonstrate the effectiveness and the state-of-the-art performance of our tracker.