Particle filter based Conjoint Individual-Group Tracker (CIGT)

Date

2015-08-28

Author

YİĞİT, Ahmet
Temizel, Alptekin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

183
views

0
downloads

In this paper, we present a method for joint tracking of individuals and groups in surveillance scenarios. Groups are dynamic entities and they may grow or shrink with merge-split events. This dynamic nature makes it difficult to track groups using conventional trackers. In this paper, we propose a new tracking method named Conjoint Individual and Group Tracker (CIGT) based on particle filter with multi-observation model and particle advection. The proposed multi-observation model uses in-group and out-group observations inspired from sociological domain and jointly models individuals and groups. Particle advection is a method used to extract flows and analyze flow stability. In this work, particle advection is integrated into the motion model of CIGT to facilitate tracking of dense groups. Experimental results show that the performance of the proposed method compares favorably with those in the literature.

Subject Keywords

Particle filters, Detectors, Computer vision, Target tracking, Optical filters, Standards

URI

https://hdl.handle.net/11511/30715

DOI

https://doi.org/10.1109/avss.2015.7301737

Conference Name

12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Visual Tracking of Objects via Rule-based Multiple Hypothesis Tracking Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2008-04-22) In this paper, one of the most crucial step of a visual surveillance system is presented. To track the multiple objects in the scene, multiple hypothesis tracking is combined with the fuzzy logic. Mixture of Gaussians method has been used to detect the moving objects in the video, which is taken from a static camera. Kalman filter has been utilized to estimate the next state of the objects. After the estimation, current measurements have been compared with the estimated features by utilizing fuzzy rules. Th...
Multi-target tracking with PHD filter using Doppler-only measurements Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut (2014-04-01) In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation...
Object Extraction and Classification in Video Surveillance Applications Civelek, Muhsin; Yazıcı, Adnan (2017-05-01) In this paper we review a number of methods used in video surveillance applications in order to detect and classify threats. Moreover, the use of those methods in wireless surveillance networks contributes to decreasing the energy consumption of the devices because it reduces the amount of information transferred through the network. In this paper we focus on the most popular object extraction and classification methods that are used in both wired and wireless surveillance applications. We also develop an a...
Object tracking for surveillance applications using thermal and visible band video data fusion Beyan, Çiğdem; Temizel, Alptekin; Department of Information Systems (2010) Individual tracking of objects in the video such as people and the luggages they carry is important for surveillance applications as it would enable deduction of higher level information and timely detection of potential threats. However, this is a challenging problem and many studies in the literature track people and the belongings as a single object. In this thesis, we propose using thermal band video data in addition to the visible band video data for tracking people and their belongings separately for ...
Visual detection and tracking of moving objects Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2007-06-13) In this paper, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Running average method has been used to detect the moving objects in the video, which is taken from a static camera. Tracking of foreground objects has been realized by using a Kalman filter. After background subtraction, morphological operators are used to remove noises detected as foreground. Active contour models (snakes) are the segmentation tools for the extracted fo...

Citation Formats

A. YİĞİT and A. Temizel, “Particle filter based Conjoint Individual-Group Tracker (CIGT),” presented at the 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IOSB, Karlsruhe Inst Technol & Fraunhofer, Karlsruhe, GERMANY, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30715.