Multi-person Tracking Strategies Based on Voxel Analysis
نویسندگان
چکیده
This paper presents two approaches to the problem of simultaneous tracking of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited to generate a discrete 3D binary representation of the foreground objects in the scene. Color information obtained from the zenithal view is added to this 3D information. The first tracking approach implements heuristic association rules between blobs labelled according to spatiotemporal connectivity criteria. Association rules are based on a cost function which considers their placement and color histogram. In the second approach, a particle filtering scheme adapted to the incoming 3D discrete data is proposed. A volume likelihood function and a discrete 3D re-sampling procedure are introduced to evaluate and drive particles. Multiple targets are tracked by means of multiple particle filters and interaction among them is modeled through a 3D blocking scheme. Evaluation over the CLEAR 2007 database yields quantitative results assessing the performance of the proposed algorithm for indoor scenarios.
منابع مشابه
Fusion of Multi-modal Sensors in a Voxel Occupancy Grid for Tracking and Behaviour Analysis
In this paper, we present a multi-modal fusion scheme for tracking and behavior analysis in Smart Home environments. This is applied to tracking multiple people and detecting their behavior. To this end, information from multiple heterogeneous sensors (visual color sensor, thermal sensor, infrared sensor and photonic mixer devices) is combined in a common 3D voxel occupancy grid. Graph cuts are...
متن کاملLong-Term Identity-Aware Multi-Person Tracking for Surveillance Video Summarization
In multi-person tracking scenarios, gaining access to the identity of each tracked individual is crucial for many applications such as long-term surveillance video analysis. Therefore, we propose a long-term multi-person tracker which utilizes face recognition information to not only enhance tracking performance, but also assign identities to tracked people. As face recognition information is n...
متن کاملTracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adv...
متن کامل3-d Least Squares Tracking in Time-resolved Tomographic Reconstruction of Dense Flow Marker Fields
Flow measurement techniques determine velocity vector fields in liquid or gas flows. In fluid mechanics, many methods are based on seeding particles to visualize the flow imaged by an adequate camera system. The tomo-PIV (tomographic particle image velocimetry) technique presented in this paper generates time-resolved volumetric reconstructions of a particle constellation from a limited number ...
متن کاملSimultaneous Pose Estimation of Multiple People using Multiple-View Cues with Hierarchical Sampling
We present a novel method for dynamic estimation of pose of multiple people using multiple video cameras. Tracking is performed using a model-based approach and a set of cues which exploit both shape and colour information. For shape we propose a fast line-search method to incorporate multi-view constraints without the computational overhead of a voxel representation. The tracking algorithm is ...
متن کامل