Visual Tracking of Objects via Rule-based Multiple Hypothesis Tracking

Date

2008-04-22

Author

Ergezer, Hamza
Leblebicioğlu, Mehmet Kemal

Metadata

Show full item record

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

Item Usage Stats

189
views

0
downloads

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. The proposed method has been tested for both single and multiple camera configurations.

Subject Keywords

Radar tracking, Kalman filters, Target tracking, Estimation, Cameras, Real time systems, Visualization

URI

https://hdl.handle.net/11511/47810

DOI

https://doi.org/10.1109/siu.2008.4632722

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Particle filter based Conjoint Individual-Group Tracker (CIGT) YİĞİT, Ahmet; Temizel, Alptekin (2015-08-28) 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-grou...
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...
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...
Performance Comparison of Target Tracking Algortihms in Underwater Environment Ege, Emre; Saranlı, Afşar (2008-04-22) Target tracking is one the most fundamental elements of a radar system. The aim of target tracking is the reliable estimation of a target's true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, bec...
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 ...

Citation Formats

H. Ergezer and M. K. Leblebicioğlu, “Visual Tracking of Objects via Rule-based Multiple Hypothesis Tracking,” 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47810.