Tracking people in crowded scenes across multiple cameras

2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1, 2005

We present an approach for persistent tracking of moving objects observed by non-overlapping and moving cameras. Our approach robustly recovers the geometry of non-overlapping views using a moving camera that pans across the scene. We address the tracking problem by modeling the appearance and motion of the moving regions. The appearance of the detected blobs is described by multiple spatial distributions models of blobs' colors and edges. This representation is invariant to 2D rigid and scale transformation. It provides a rich description of the detected regions, and produces an efficient blob similarity measure for tracking. The motion model is obtained using a Kalman Filter (KF) process, which predicts the position of the moving objects while taking into account the camera motion. Tracking is performed by the maximization of a joint probability model combining objects' appearance and motion. The novelty of our approach consists in defining a spatio-temporal Joint Probability Data Association Filter (JPDAF) for integrating multiple cues. The proposed method tracks a large number of moving people with partial and total occlusions and provides automatic handoff of tracked objects. We demonstrate the performance of the system on several real video surveillance sequences.

18th International Conference on Pattern Recognition (ICPR'06), 2006

This paper introduces a multiple human objects tracking system to detect and track multiple objects in the crowded scene in which occlusions occur. Our method assign each pixel to different human object based on its relative distance to that object and the corresponding color model. If no occlusion, we easily track each object independently based on each segmented object region and optical flow. With occlusion, we analyze the color distribution of the occlusion group to differentiate each object in the group. By calculating the distances between objects, we can determine whether an object is separated from the occlusion group and to be tracked individually afterwards.

2010 6th International Conference on Emerging Technologies (ICET), 2010

The aim of this paper is to present an algorithm for multiple object tracking and video summarization in a scene filmed by one or several cameras. We propose a computationally efficient real time human tracking algorithm, which can 1) track objects inside the field of view (FOV) of a camera even in case of occlusions; 2) recognize objects that quit and then return on a camera's FOV; 3) recognize objects passing through different cameras FOV. We propose a simple 1-D appearance model, called vertical feature (VF), view and size invariant, which is stored in a database in order to help object recognition. We combine it with other motion features like position and velocity for real-time tracking. We find the k closest matches of current object and select the one whose predicted position is closest to the current object position. Our algorithm shows good capabilities for objects tracking even with the change of object view angle and also with the partial change of shape. We compare our algorithm with appearance based and motion based algorithms and show the advantage of a combined approach.

Human Motion–Understanding, Modeling, Capture …, 2007

International Symposium on Visual Computing, 2010

This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.

Tracking of humans in dynamic scenes has been an important topic of research. Most techniques, however, are limited to situations where humans appear isolated and occlusion is small. Typical methods rely on appearance models that must be acquired when the humans enter the scene and are not occluded. We present a method that can track humans in crowded environments, with significant and persistent occlusion by making use of human shape models in addition to camera models, the assumption that humans walk on a plane and acquired appearance models. Experimental results and a quantitative evaluation are included.

International Journal of …, 2011

The proposed approach aims to track multiple moving people in a colour video acquired with a single camera. The first phase of the approach consists in precisely detecting multi-human inside moving foregrounds. The input to this phase is foreground pixels which were extracted from the scene using any background subtraction technique. These moving foregrounds are then further segmented into multiple moving people using region segmentation and shape-based occlusion handling. The second phase assigns the detected human blobs to tracks using robust matching process based both on appearance model and motion model. For this, we use Kalman filter to predict future locations and sizes for dynamic persons and fuse this information with appearance-based comparison in order to assign each blob to a track. The preliminary experiments on several representative sequences have shown that this unsupervised approach can robustly detect and track multiple occluded moving persons, even at lower temporal resolution.

Visual Information Processing XXI, 2012

Video cameras are widely used for monitoring public areas, such as train stations, airports and shopping centers. When crowds are dense, automatically tracking individuals becomes a challenging task. We propose a new tracker which employs a particle filter tracking framework, where the state transition model is estimated by an optical-flow algorithm. In this way, the state transition model directly uses the motion dynamics across the scene, which is better than the traditional way of a pre-defined dynamic model. Our result shows that the proposed tracker performs better on different tracking challenges compared with the state-of-the-art trackers, while also improving on the quality of the result.

A computer vision system for tracking multiple people in relatively unconstrained environments is described. For the purpose of efficiently tracking multiple people in the presence of occlusions, we propose: (i) to combine blob matching with particle filtering, and (ii) to augment these tracking algorithms with a novel colour appearance model. The proposed system efficiently counteracts the shortcomings of the two algorithms by switching from one to the other during occlusions. Results on public datasets as well as real surveillance videos from a metropolitan railway station demonstrate the efficacy of the proposed system.

1998

A combined 2D, 3D approach is presented that allows for robust tracking of moving bodies in a given environment as observed via a single, uncalibrated video camera. Lowlevel features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that combines lowlevel (image processing) and mid-level (recursive trajectory estimation) information obtained during the tracking process. The resulting system can segment and maintain the tracking of moving objects before, during, and after occlusion. At each frame, the system also extracts a stabilized coordinate frame of the moving objects. This stabilized frame can be used as input to motion recognition modules. The approach enables robust tracking without constraining the system to know the shape of the objects being tracked beforehand; although, some assumptions are made about the characteristics of the shape of the objects, and how they evolve with time. Experiments in tracking moving people are described.