Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling

Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years. However, conventional methods tend to use a two-stage sampling paradigm, in which the search space needs to be uniformly explored with an inefficient preliminary sampling phase. In this paper, we propose a novel sampling-based method in the Bayesian filtering framework to address the problem. Within the framework, nearest neighbor field estimation is utilized to compute the importance proposal probabilities, which guide the Markov chain search towards promising regions and thus enhance the sampling efficiency; given the motion priors, a smoothing stochastic sampling Monte Carlo algorithm is proposed to approximate the posterior distribution through a smoothing weight-updating scheme. Moreover, to track the abrupt and the smooth motions simultaneously, we develop an abrupt-motion detection scheme which can discover the presence of abrupt motions during online tracking. Extensive experiments on challenging image sequences demonstrate the effectiveness and the robustness of our algorithm in handling the abrupt motions.Comment: submitted to Elsevier Neurocomputin

Saliency Based Tracking Method for Abrupt Motions via Two-stage Sampling

针对运动突变目标视觉跟踪问题,提出一种基于视觉显著性的两阶段采样跟踪算法.首先,将视觉显著性信息引入到WAng-lAndAu蒙特卡罗(WAng-lAndAu MOnTE CArlO,WlMC)跟踪算法中,设计了结合显著性先验的接受函数,利用子区域的显著性值来引导马尔可夫链的构造,通过增大目标出现区粒子的接受概率,提高采样效率;其次,针对运动序列中平滑与突变运动共存的特点,建立两阶段采样模型.其中第一阶段对目标当前运动类型进行判定,第二阶段则根据判定结果采用相应算法.突变运动采用基于视觉显著性的WlMC算法,平滑运动采用双链马尔可夫链蒙特卡罗(MArkO CHAIn MOnTE CArlO,MCMC)算法,以此完成目标跟踪,提高算法的鲁棒性.该算法既避免了目标在平滑运动时全局采样导致精度下降的缺点,又能在目标发生运动突变时有效捕获目标.实验结果表明,该算法不仅能有效处理运动突变目标的跟踪问题,在典型图像序列上也具有良好的鲁棒性.In this paper, a saliency based tracking method via two-stage sampling is proposed for abrupt motions.Firstly, the visual salience is introduced as a prior knowledge into the Wang-Landau Monte Carlo(WLMC)-based tracking algorithm.By dividing the spatial space into disjoint sub-regions and assigning each sub-region a saliency value, a prior knowledge of the promising regions is obtained; then the saliency values of sub-regions are integrated into the Markov chain Monte Carlo(MCMC) acceptance mechanism to guide effective states sampling.Secondly, considering the abrupt motion sequence contains both abrupt and smooth motions, a two-stage sampling model is brought up into the algorithm.In the first stage, the model detects the motion type of the target.According to the result of the first stage, the model chooses either the saliency-based WLMC method to track abrupt motions or the double-chain MCMC method to track smooth motions of the target in the second stage.The algorithm efficiently addresses tracking of abrupt motions while smooth motions are also accurately tracked.Experimental results demonstrate that this approach outperforms the state-of-the-art algorithms on abrupt motion sequence and public benchmark sequence in terms of accuracy and robustness.国家自然科学基金(61373077); 国防基础科研计划(B0110155); 国防科技重点实验室基金(9140C30211ZS8); 高等学校博士学科点专项科研基金(20110121110020)资助~

Wi-Fi based people tracking in challenging environments

People tracking is a key building block in many applications such as abnormal activity detection, gesture recognition, and elderly persons monitoring. Video-based systems have many limitations making them ineffective in many situations. Wi-Fi provides an easily accessible source of opportunity for people tracking that does not have the limitations of video-based systems. The system will detect, localise, and track people, based on the available Wi-Fi signals that are reflected from their bodies. Wi-Fi based systems still need to address some challenges in order to be able to operate in challenging environments. Some of these challenges include the detection of the weak signal, the detection of abrupt people motion, and the presence of multipath propagation. In this thesis, these three main challenges will be addressed. Firstly, a weak signal detection method that uses the changes in the signals that are reflected from static objects, to improve the detection probability of weak signals that are reflected from the person’s body. Then, a deep learning based Wi-Fi localisation technique is proposed that significantly improves the runtime and the accuracy in comparison with existing techniques. After that, a quantum mechanics inspired tracking method is proposed to address the abrupt motion problem. The proposed method uses some interesting phenomena in the quantum world, where the person is allowed to exist at multiple positions simultaneously. The results show a significant improvement in reducing the tracking error and in reducing the tracking delay