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3D human motion analysis from a single viewpoint is an extremely challenge computer vision task due to the lack of depth information and complex human movements. To resolve these problems, based on the quantum computing and immune clonal operator, a novel evolution algorithm, called a quantum-behaved clonal algorithm (QBCA), is proposed for 3D human motion analysis. Firstly, a 2D part-based human detector (PBD) is used to compute the 2D landmarks of key body joints. Then, human motion analysis is performed by optimizing a distance similarity function between the detected 2D landmarks and 2D projection of predicted 3D joint points using QBCA. Moreover, our method not only has a good balance between exploitation and exploration, but also searches both local optimum solution and global optimum solution, simultaneously. Extensive results on PARSE and HumanEva dataset demonstrate the robustness and effectiveness of our proposed method.

Although the classic TLD (tracking-learning-detection) target tracking algorithm can track a single target robustly in a long period, its real-time performance is poor. CT (Compressing Tracking) real-time tracking algorithm is real-time and efficient, but it cannot accurately track the scale-changing targets. Aiming at handling both methods' shortcomings, this paper proposes an improved TLD tracking method based on the tracking by variable-scale compressing. This method uses variable-scale compression detection instead of TLD detector and uses P-N(positive-negative) learning method to update CT detector's classifier. Experimental results show that the proposed algorithm presents better performance in robustness and real-time tracking.