Multiple object tracking using an automatic veriable-dimension particle filter

Object tracking through particle filtering has been widely addressed in recent years. However, most works assume a constant number of objects or utilize an external detector that monitors the entry or exit of objects in the scene. In this work, a novel tracking method based on particle filtering that is able to automatically track a variable number of objects is presented. As opposed to classical prior data assignment approaches, adaptation of tracks to the measurements is managed globally. Additionally, the designed particle filter is able to generate hypotheses on the presence of new objects in the scene, and to confirm or dismiss them by gradually adapting to the global observation. The method is especially suited for environments where traditional object detectors render noisy measurements and frequent artifacts, such as that given by a camera mounted on a vehicle, where it is proven to yield excellent results

Behavior Extraction from Examples Using Federate MCMC-Based Particle Filtering

AbstractData-driven methods of simulating a crowd of virtual humans that exhibit behaviors imitating real human crowds play an important role in crowd simulation. In this paper, we propose a Bayesian framework for the extraction of real human's behaviors which exhibit interactions in their daily life using multiple fixed cameras. The described Markov chain Monte Carlo particle filter can effectively deals with interacting targets which are influenced by the proximity and behaviors of other targets. In this paper, we use a Markov random field motion prior combing with a federate filter algorithm which treats the observations discriminatorily to substantially improve the tracking of a fixed number of interacting targets. Simultaneously, we replace the traditional importance sampling step with MCMC sampling step to get over the vast computational requirements for large numbers of targets. i.e., we focus on the data fusion and the behavior recognition process. Finally, experimental results demonstrate that the proposed Bayesian framework deals efficiently and effectively with extractions of interacting behavior

Simultaneous 3D object tracking and camera parameter estimation by Bayesian methods and transdimensional MCMC sampling

Multi-camera 3D tracking systems with overlapping cameras represent a powerful mean for scene analysis, as they potentially allow greater robustness than monocular systems and provide useful 3D information about object location and movement. However, their performance relies on accurately calibrated camera networks, which is not a realistic assumption in real surveillance environments. Here, we introduce a multi-camera system for tracking the 3D position of a varying number of objects and simultaneously refin-ing the calibration of the network of overlapping cameras. Therefore, we introduce a Bayesian framework that combines Particle Filtering for tracking with recursive Bayesian estimation methods by means of adapted transdimensional MCMC sampling. Addi-tionally, the system has been designed to work on simple motion detection masks, making it suitable for camera networks with low transmission capabilities. Tests show that our approach allows a successful performance even when starting from clearly inaccurate camera calibrations, which would ruin conventional approaches

Stereo Vision Tracking of Multiple Objects in Complex Indoor Environments

This paper presents a novel system capable of solving the problem of tracking multiple targets in a crowded, complex and dynamic indoor environment, like those typical of mobile robot applications. The proposed solution is based on a stereo vision set in the acquisition step and a probabilistic algorithm in the obstacles position estimation process. The system obtains 3D position and speed information related to each object in the robot’s environment; then it achieves a classification between building elements (ceiling, walls, columns and so on) and the rest of items in robot surroundings. All objects in robot surroundings, both dynamic and static, are considered to be obstacles but the structure of the environment itself. A combination of a Bayesian algorithm and a deterministic clustering process is used in order to obtain a multimodal representation of speed and position of detected obstacles. Performance of the final system has been tested against state of the art proposals; test results validate the authors’ proposal. The designed algorithms and procedures provide a solution to those applications where similar multimodal data structures are found

Tracking Interacting Objects Optimally Using Integer Programming

In this paper, we show that tracking different kinds of interacting objects can be formulated as a network-flow Mixed Integer Program. This is made possible by tracking all objects simultaneously and expressing the fact that one object can appear or disappear at locations where another is in terms of linear flow constraints. We demonstrate the power of our approach on scenes involving cars and pedestrians, bags being carried and dropped by people, and balls being passed from one player to the next in a basketball game. In particular, we show that by estimating jointly and globally the trajectories of different types of objects, the presence of the ones which were not initially detected based solely on image evidence can be inferred from the detections of the others

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~

Seguimiento de múltiples objetos basado en el algoritmo de Viterbi

El seguimiento de objetos en secuencias de imágenes es actualmente un tema investigación importante debido a que tiene un amplio rango de aplicaciones tales como video vigilancia, análisis deportivo, etc. Un ejemplo común es el análisis de jugadores en un partido de fútbol. Mediante el procesamiento de las imágenes se puede establecer la trayectoria de cada jugador durante el partido y así proveer información importante sobre su actividad. El problema del seguimiento de objetos tiene dos grandes pasos principales, el primero es detectar y localizar los objetos dentro de los fotogramas del video y el segundo es la parte de seguimiento, esto implica implementar un método que obtenga las trayectorias de los objetos detectados resolviendo las oclusiones que pueden establecer entre ellos. En este trabajo se propone un método para el seguimiento de múltiples objetos. Se parte de un trabajo previo donde se detectó a los jugadores en la imagen y se estableció la localización de todos ellos en el terreno de juego, afrontando el segundo problema explicado, es decir, la asignación de una etiqueta inequívoca para cada jugador a lo largo de todo el partido. Para llevar a cabo esta tarea previamente se ha procedido a un etiquetado manual de todos los jugadores para posteriormente verificar la fiabilidad del método propuesto. El método planteado sigue un análisis de probabilidades de presencia de cada jugador en una posición determinada del campo y un método robusto de asignación temporal de todas las posiciones de los jugadores mediante el algoritmo de Viterbi