Context Aware Deep Feature Compression for High Speed Visual Tracking
We propose a new context aware correlation filter based tracking framework to achieve both high computational speed and state of the art performance among real time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a context aware scheme utilizing multiple expert auto encoders; a context in our framework refers to the coarse category of the tracking target according to appearance patterns. In the pre training phase, one expert auto encoder is trained per category. In the tracking phase, the best expert auto encoder is selected for a given target, and only this auto encoder is used. To achieve high tracking performance with the compressed feature map, we introduce extrinsic denoising processes and a new orthogonality loss term for pre training and fine tuning of the expert autoencoders. We validate the proposed context aware framework through a number of experiments, where our method achieves a comparable performance to state of the art trackers which cannot run in real time, while running at a significantly fast speed of over 100 fps.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition