Real time multi-object tracking using multiple cameras Semester Project Teaching Assistant Horesh Ben Shitrit (original) (raw)

Tracking people with probabilistic appearance models

2002

This paper describes a real-time computer vision system for tracking people in monocular video sequences. The system tracks people as they move through the camera's field of view, by a combination of background subtraction and the learning of appearance models. The appearance models allow objects to be tracked through occlusions using a probabilistic pixel reclassification algorithm. The system is evaluated on the three test sequences of the PETS 2002 dataset, for which tracking results and processing time requirements are presented.

Online multiple people tracking-by-detection in crowded scenes

7'th International Symposium on Telecommunications (IST'2014), 2014

Multiple people detection and tracking is a challenging task in real-world crowded scenes. In this paper, we have presented an online multiple people tracking-by-detection approach with a single camera. We have detected objects with deformable part models and a visual background extractor. In the tracking phase we have used a combination of support vector machine (SVM) person-specific classifiers, similarity scores, the Hungarian algorithm and inter-object occlusion handling. Detections have been used for training person-specific classifiers and to help guide the trackers by computing a similarity score based on them and spatial information and assigning them to the trackers with the Hungarian algorithm. To handle inter-object occlusion we have used explicit occlusion reasoning. The proposed method does not require prior training and does not impose any constraints on environmental conditions. Our evaluation showed that the proposed method outperformed the state of the art approaches by 10% and 15% or achieved comparable performance

Multicamera People Tracking with a Probabilistic Occupancy Map

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes.

Multi-camera people tracking using evidential filters

International Journal of Approximate Reasoning, 2009

This work proposes a novel filtering algorithm that constitutes an extension of Bayesian particle filters to the Dempster-Shafer theory. Our proposal solves the multi-target problem by combining evidences from multiple heterogeneous and unreliable sensors. The modelling of uncertainty and absence of knowledge in our approach is specially attractive since it does not require to specify prior nor conditionals that might be difficult to obtain in complex problems.

Real-time tracking of multiple persons

2003

Robust tracking of persons in real-world environments and in real-time is a common goal in many video applications. In this paper a computational system for the real-time tracking of multiple persons in natural environments is presented. The system integrates state-of-the-art methodologies for the analysis of movement and color, as well as for the detection of faces. Face detection is complemented by a face tracking module based on heuristics developed by the authors. Exemplary results of the integrated system working in real-world video sequences are shown.

Tracking of persons for video surveillance of unattended environments

2008 19th International Conference on Pattern Recognition, 2008

This paper describes a visual surveillance system for remote monitoring of unattended environments. 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.

Multiple-Object Tracking in Cluttered and Crowded Public Spaces

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.

Multiple People Detection and Tracking

Mantech Publications , 2021

Multiple people detection in real-time is still a challenging task despite having different techniques. It is challenging because partially occluded people are still often not recognized in a heavily populated area, and also due to Non-Maximum suppression, correct bounding boxes are also discarded, which leads to imprecision in the detections. This paper presents the various modifications done to multiple people detection and tracking algorithms, which improves the efficiency and accuracy of the previously used cases.

A combined motion and appearance model for human tracking in multiple cameras environment

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.