Fast Object Tracking in Intelligent Surveillance System

Authors:
Ki-Yeol Eom;Tae-Ki Ahn;Gyu-Jin Kim;Gyu-Jin Jang;Moon-Hyun Kim
Affiliations:
Department of Information and Communication Engineering, SungKyunKwan University, Suwon, Korea 440-746;Korea Railroad Research Institute, Korea 437-757;Department of Information and Communication Engineering, SungKyunKwan University, Suwon, Korea 440-746;Department of Information and Communication Engineering, SungKyunKwan University, Suwon, Korea 440-746;Department of Information and Communication Engineering, SungKyunKwan University, Suwon, Korea 440-746
Venue:
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Year:
2009

Citing 11
Cited 0

W4: Real-Time Surveillance of People and Their Activities

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
Performance Evaluation of Object Detection Algorithms

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Detecting Moving Shadows: Algorithms and Evaluation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of global image thresholding for change detection

Pattern Recognition Letters
An accurate and fast point-to-plane registration technique

Pattern Recognition Letters
An efficient and accurate method for evaluating time series similarity

Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Performance evaluation metrics and statistics for positional tracker evaluation

ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Objective evaluation of video segmentation quality

IEEE Transactions on Image Processing
Performance measures for video object segmentation and tracking

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present the improved Fast Time Series Evaluation(IFTSE) algorithm that can fast and efficiently track the objects in bad environment conditions. Object tracking in intelligent surveillance system is an important part to identify suspicious objects' behavior. But object tracking is exhaustive and time-consuming process and we cannot also efficiently search the trajectory of detected objects due to bad conditions (e.g. bad camera capacity, dust particles in the air, lighting changes). To demonstrate the performance of the proposed IFTSE algorithm for tracking the objects, we introduce evaluation metrics. A prototype tracking system that IFTSE algorithm is employed is implemented using Visual C++. We increase true positive rate by approximately 6% and reduce false alarm rate by approximately 2% and reduce id change by approximately 30%.