Discriminative Hash Tracking With Group Sparsity
提出一种基于判别性监督哈希算法的跟踪框架，将跟踪视为二进制空间中的目标匹配问题，通过联合学习哈希码和哈希函数，并引入组稀疏性动态选择特征，在多个挑战性图像序列上验证了有效性。
In this paper, we propose a novel tracking framework based on discriminative supervised hashing algorithm. Different from previous methods, we treat tracking as a problem of object matching in a binary space. Using the hash functions, all target templates and candidates are mapped into compact binary codes, with which the target matching is conducted effectively. To be specific, we make full use of the label information to assign a compact and discriminative binary code for each sample. And to deal with out-of-sample case, multiple hash functions are trained to describe the learned binary codes, and group sparsity is introduced to the hash projection matrix to select the representative and discriminative features dynamically, which is crucial for the tracker to adapt to target appearance variations. The whole training problem is formulated as an optimization function where the hash codes and hash function are learned jointly. Extensive experiments on various challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.