No abstract

In this paper, a novel method for people detection and tracking is proposed, based on stereo vision. Each person is represented by a group of the feature points. In this method feature point extraction and 2D space construction of projected points on the ground plane is performed in order to provide top view. Occlusion, as a main challenge in tracking systems, can be addressed by top view scene. A robust kernel density estimation method is employed to categorize points. Then Kalman filter is applied to reduce the detection computation complexity from second frame by predicting center of the groups in the next frame. Our method is more practical than existing methods since it has lower computation cost of detection, because of using feature extraction instead of depth map. This low computational complexity makes our method suitable to be used in real time applications.

Single-particle tracking (SPT) has become an important method to bridge biochemistry and cell biology since it allows direct observation of protein binding and diffusion dynamics in live cells. However, accurately inferring information from SPT studies is challenging due to biases in both data analysis and experimental design. To address analysis bias, we introduce 'Spot-On', an intuitive web-interface. Spot-On implements a kinetic modeling framework that accounts for known biases, including molecules moving out-of-focus, and robustly infers diffusion constants and subpopulations from pooled single-molecule trajectories. To minimize inherent experimental biases, we implement and validate stroboscopic photo-activation SPT (spaSPT), which minimizes motion-blur bias and tracking errors. We validate Spot-On using experimentally realistic simulations and show that Spot-On outperforms other methods. We then apply Spot-On to spaSPT data from live mammalian cells spanning a wide range of nuclear dynamics and demonstrate that Spot-On consistently and robustly infers subpopulation fractions and diffusion constants.

In recent years, Compressive Sensing (CS) or sparse representation has been considered as one of the most favorite topics in the areas of Computer Vision. In particular this theory can be widely applied in Visual Tracking applications. Addressing the problem of sparse representation through minimizations methods can play a dominant role in the CS trackers (trackers based on CS theory). In contrast to the previous algorithms which usually solve the problem of minimization by using L1-norm, L0-norm minimization is used directly to achieve sparseness in our proposed method. Simulations and results demonstrate that the proposed method can achieve the same or better accuracy with many less complexity than traditional algorithms which used interior-point method.