Two dimensional hashing for visual tracking

• In this paper, we propose a 2D-based hashing method which could fast extract the binary feature of samples.
• We successfully apply the hashing method into tracking model by some details.
• We design an effective and suitable learning model to update hash functions at every frame.
• Comparison experiments are done to demonstrate the effectiveness and efficiency of our tracker.

Appearance model is a key part of tracking algorithms. To attain robustness, many complex appearance models are proposed to capture discriminative information of object. However, such models are difficult to maintain accurately and efficiently. In this paper, we observe that hashing techniques can be used to represent object by compact binary code which is efficient for processing. However, during tracking, online updating hash functions is still inefficient with large number of samples. To deal with this bottleneck, a novel hashing method called two dimensional hashing is proposed. In our tracker, samples and templates are hashed to binary matrices, and the hamming distance is used to measure confidence of candidate samples. In addition, the designed incremental learning model is applied to update hash functions for both adapting situation change and saving training time. Experiments on our tracker and other eight state-of-the-art trackers demonstrate that the proposed algorithm is more robust in dealing with various types of scenarios.