Robust Visual Tracking Based on Improved Perceptual Hashing for Robot Vision

In this paper, perceptual hash codes are adopted as appearance models of objects for visual tracking. Based on three existing basic perceptual hashing techniques, we propose Laplace-based hash (LHash) and Laplace-based difference hash (LDHash) to efficiently and robustly track objects in challenging video sequences. By qualitative and quantitative comparison with previous representative tracking methods such as mean-shift and compressive tracking, experimental results show perceptual hashing-based tracking outperforms and the newly proposed two algorithms perform the best under various challenging environments in terms of efficiency, accuracy and robustness. Especially, they can overcome severe challenges such as illumination changes, motion blur and pose variation

New fusional framework combining sparse selection and clustering for key frame extraction

Key frame extraction can facilitate rapid browsing and efficient video indexing in many applications. However, to be effective, key frames must preserve sufficient video content while also being compact and representative. This study proposes a syncretic key frame extraction framework that combines sparse selection (SS) and mutual information‐based agglomerative hierarchical clustering (MIAHC) to generate effective video summaries. In the proposed framework, the SS algorithm is first applied to the original video sequences to obtain optimal key frames. Then, using content‐loss minimisation and representativeness ranking, several candidate key frames are efficiently selected and grouped as initial clusters. A post‐processor – an improved MIAHC – subsequently performs further processing to eliminate redundant images and generate the final key frames. The proposed framework overcomes issues such as information redundancy and computational complexity that afflict conventional SS methods by first obtaining candidate key frames instead of accurate key frames. Subsequently, application of the improved MIAHC to these candidate key frames rather than the original video not only results in the generation of accurate key frames, but also reduces the computation time for clustering large videos. The results of comparative experiments conducted on two benchmark datasets verify that the performance of the proposed SS–MIAHC framework is superior to that of conventional methods