Once for All: a Two-flow Convolutional Neural Network for Visual Tracking
This addresses the problem of slow, object-specific training in visual tracking for applications like surveillance or robotics, offering a more efficient, generic solution.
The paper tackles the challenge of arbitrary object appearance in visual tracking by proposing a two-flow convolutional neural network (YCNN) that measures similarity between object and search patches, enabling tracking of all objects without retraining and achieving 45 frames-per-second speed with outstanding performance on 51 sequences.
One of the main challenges of visual object tracking comes from the arbitrary appearance of objects. Most existing algorithms try to resolve this problem as an object-specific task, i.e., the model is trained to regenerate or classify a specific object. As a result, the model need to be initialized and retrained for different objects. In this paper, we propose a more generic approach utilizing a novel two-flow convolutional neural network (named YCNN). The YCNN takes two inputs (one is object image patch, the other is search image patch), then outputs a response map which predicts how likely the object appears in a specific location. Unlike those object-specific approach, the YCNN is trained to measure the similarity between two image patches. Thus it will not be confined to any specific object. Furthermore the network can be end-to-end trained to extract both shallow and deep convolutional features which are dedicated for visual tracking. And once properly trained, the YCNN can be applied to track all kinds of objects without further training and updating. Benefiting from the once-for-all model, our algorithm is able to run at a very high speed of 45 frames-per-second. The experiments on 51 sequences also show that our algorithm achieves an outstanding performance.