In this paper we address the problem of detection and tracking of pedestrians in complex scenarios. The inclusion of prior knowledge is more and more crucial in scene analysis to guarantee flexibility and robustness, necessary to have reliability in complex scenes. We aim to combine image processing methods with behavioral models of pedestrian dynamics, calibrated on real data. We introduce Discrete Choice Models (DCM) for pedestrian behavior and we discuss their integration in a detection and tracking context. The obtained results show how it is possible to combine both methodologies to improve the performances of such systems in complex sequences.

Abstract—This paper presents a method which avoids the common practice of using a complex joint state-space representation and performing tedious joint data association for multiple object tracking applications. Instead, we propose a distributed Bayesian formulation using multiple interactive trackers that requires much lower complexity for real-time tracking applications. When the objects ’ observations do not interact with each other, our approach performs as multiple independent trackers. However, when the objects ’ observations exhibit interaction, defined as close proximity or partial and complete occlusion, we extend the conventional Bayesian tracking framework by modeling such interaction in terms of potential functions. The proposed “magnetic-inertia” model represents the cumulative effect of virtual physical forces that objects undergo while interacting with each other. It implicitly handles the “error merge ” and “object labeling” problems and thus solves the difficult object occlusion and data association problems in an innovative way. Our preliminary simulations have demonstrated that the proposed approach is far superior to other methods in both robustness and speed. Index Terms—Bayesian tracking, data association, multiple object tracking, object occlusion, particle filter. I.

In this paper we propose a method for the automatic counting of pedestrians in video sequences for (automatic) video surveillance applications. We analyse the trajectory data set provided by a detection/tracking system. When using classical target detection and tracking systems, it is weel known that the number of detected targets is overestimated/underestimated. A better representation for the trajectories is given in the ICA (Independent Component Analysis) transformed domain and clustering techniques are applied to the ICA-transformed data in order to provide a better estimation of the actual number of pedestrians which are present on the scene. 1.

Many real world applications in computer and multimedia such as augmented reality and environmental imaging require an elastic accurate contour around a tracked object. In the first part of the paper we introduce a novel tracking algorithm that combines a motion estimation technique with the Bayesian Importance Sampling framework. We use Adaptive Block Matching (ABM) as the motion estimation technique. We construct the proposal density from the estimated motion vector. The resulting algorithm requires a small number of particles for efficient tracking. The tracking is adaptive to different categories of motion even with a poor a priori knowledge of the system dynamics. Particulary off-line learning is not needed. A parametric representation of the object is used for tracking purposes. In the second part of the paper, we refine the tracking output from a parametric sample to an elastic contour around the object. We use a 1D active contour model based on a dynamic programming scheme to refine the output of the tracker. To improve the convergence of the active contour, we perform the optimization over a set of randomly perturbed initial conditions. Our experiments are applied to head tracking. We report promising tracking results in complex environments.

Social interaction plays an important role in our daily lives. It is one of the most important indicators of physical or mental changes in aging patients. In this paper, we investigate the problem of detecting social interaction patterns of patients in a skilled nursing facility using audio/visual records. Our studies consist of both a “wizard of Oz ” study and an experimental study of various sensors and detection models for detecting and summarizing social interactions among aging patients and caregivers. We first simulate plausible sensors using human labeling on top of audio and visual data collected from a skilled nursing facility. The most useful sensors and robust detection models are determined using the simulated sensors. We then present the implementation of some real sensors based on video and audio analysis techniques and evaluate the performance of these implementations in detecting interaction. We conclude the paper with discussions and future work.

Porting well known computer vision algorithms to low power, high performance computing devices such as SIMD linear processor arrays can be a challenging task. One especially useful such algorithm is the color-based particle lter, which has been applied successfully by many research groups to the problem of tracking nonrigid objects. In this paper, we propose an implementation of the color-based particle lter suitable for SIMD processors. The main focus of our work is on the parallel computation of the particle weights. This step is the major bottleneck of standard implementations of the color-based particle lter since it requires the knowledge of the histograms of the regions surrounding each hypothesized target position. We expect this approach to perform faster in an SIMD processor than an implementation in a standard desktop computer even running at much lower clock speeds. 1.

Abstract—This paper presents a novel particle allocation approach to particle filtering which minimizes the total tracking distortion for a fixed number of particles over a video sequence. We define the tracking distortion as the variance of the error between the true state and estimated state and use rate-distortion theory to determine the optimal particle number and memory size allocation under fixed particle number and memory constraints, respectively. We subsequently provide an algorithm for simultaneous adjustment of the proposal variance and particle number for optimal particle allocation in video tracking systems. Experimental results are used to evaluate the proposed video tracking system and demonstrate its utility for target tracking in numerical examples and video sequences. We demonstrate the superiority of the proposed dynamic proposal variance and optimal particle allocation algorithm in comparison to traditional particle allocation methods, i.e., a fixed number of particles per frame. Index Terms—Dynamic proposal variance, optimal particle allocation, particle filter, tracking distortion, video tracking. I.

We present a novel framework for tracking of a long sequence of human activities, including the time instances of change from one activity to the next, using a closed-loop, non-linear dynamical feedback system. A composite feature vector describing the shape, color and motion of the objects, and a non-linear, piecewise stationary, stochastic dynamical model describing its spatio-temporal evolution, are used for tracking. The tracking error or expected log likelihood, which serves as a feedback signal, is used to automatically detect changes and switch between activities happening one after another in a long video sequence. Whenever a change is detected, the tracker is reinitialized automatically by comparing the input image with learned models of the activities. Unlike some other approaches that can track a sequence of activities, we do not need to know the transition probabilities between the activities, which can be difficult to estimate in many application scenarios. We demonstrate the effectiveness of the method on multiple indoor and outdoor real-life videos and analyze its performance. 1.

In the visual tracking domain, Particle Filtering (PF) can become quite inefficient when being applied into high dimensional state space. Rao-Blackwellisation [1] has been shown to be an effective method to reduce the size of the state space by marginalizing out some of the variables analytically [2]. In this paper based on our previous work [3] we propose an RBPF tracking algorithm with adaptive system noise model. Experiments using both simulation data and real data show that the proposed RBPF algorithm with adaptive noise variance improves its performance significantly over conventional Particle Filter tracking algorithm. The improvements manifest in three aspects: increased estimation accuracy, reduced variance for estimates and reduced particle numbers are needed to achieve the same level of accuracy. The last two performance improvements are evaluated in this paper using simulation data. 1.

Abstract. Robust tracking of objects in video is a key challenge in computer vision with applications in automated surveillance, video indexing, human-computer-interaction, gesture recognition, traffic monitoring, etc. Many algorithms have been developed for tracking an object in controlled environments. However, they are susceptible to failure when the challenge is to track multiple objects that undergo appearance change to due to factors such as variation in illumination and object pose. In this paper we present a tracker based on Bayesian estimation, which is relatively robust to object appearance change, and can track multiple targets simultaneously in real time. The object model for computing the likelihood function is incrementally updated and uses background-foreground segmentation information to ameliorate the problem of drift associated with object model update schemes. We demonstrate the efficacy of the proposed method by tracking objects in image sequences from the CAVIAR dataset. 1