scholarly journals An Object Detection and Tracking System using Fuzzy C-means and CONDENSATION

2011 ◽  
Vol 16 (4) ◽  
pp. 87-98 ◽  
Author(s):  
Jong-Ho Kim ◽  
Sang-Kyoon Kim ◽  
Goo-Seun Hang ◽  
Sang-Ho Ahn ◽  
Byoung-Doo Kang
Keyword(s):  
Object Detection ◽  
Tracking System ◽  
Fuzzy C Means ◽  
Object Detection And Tracking ◽  
Detection And Tracking

Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

2011 ◽  
pp. 1263-1284
Author(s):  
Ruth Aguilar-Ponce ◽  
Ashok Kumar ◽  
J. Luis Tecpanecatl-Xihuitl ◽  
Magdy Bayoumi ◽  
Mark Radle
Keyword(s):  
Wireless Sensor Network ◽  
Object Detection ◽  
Sensor Network ◽  
Tracking System ◽  
Visual Surveillance ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Automated Object Detection

The aim of this research was to apply an agent approach to wireless sensor network in order to construct a distributed, automated scene surveillance. Wireless sensor network using visual nodes is used as a framework for developing a scene understanding system to perform smart surveillance. Current methods of visual surveillance depend on highly train personnel to detect suspicious activity. However, the attention of most individuals degrades after 20 minutes of evaluating monitor-screens. Therefore current surveillance systems are prompt to failure. An automated object detection and tracking was developed in order to build a reliable visual surveillance system. Object detection is performed by means of a background subtraction technique known as Wronskian change detection. After discovery, a multi-agent tracking system tracks and follows the movement of each detected object. The proposed system provides a tool to improve the reliability and decrease the cost related to the personnel dedicated to inspect the monitor-screens

scholarly journals Spatial pyramid context-aware moving object detection and tracking for full motion video and wide aerial motion imagery

2017 ◽  
Author(s):  
◽  
Mahdieh Poostchi
Keyword(s):  
Object Detection ◽  
Tracking System ◽  
Moving Object Detection ◽  
Moving Object ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Motion Imagery ◽  
Integral Histogram ◽  
Motion Video ◽  
Spatial Pyramid

A robust and fast automatic moving object detection and tracking system is essential to characterize target object and extract spatial and temporal information for different functionalities including video surveillance systems, urban traffic monitoring and navigation, robotic, medical imaging, etc. A reliable detecting and tracking system is required to generalize across huge variations in object appearance changes due to camera viewpoint, pose, scale, lighting conditions, imaging quality or occlusions and achieve real-time performance. In this dissertation, I present a collaborative Spatial Pyramid Context-aware moving object detection and Tracking system (SPCT). The proposed visual tracker is composed of one master tracker that usually relies on visual object features and two auxiliary trackers based on object temporal motion information that will be called dynamically to assist master tracker. SPCT utilizes image spatial context at different level to make the video tracking system resistant to occlusion, background noise and improve target localization accuracy and robustness. We chose a pre-selected seven-channel complementary features including RGB color, intensity and spatial pyramid of HoG to encode object color, shape and spatial layout information. We exploit integral histogram as building block to meet the demands of real-time performance. A novel fast algorithm is presented to accurately evaluate spatially weighted local histograms in constant time complexity using an extension of the integral histogram method. Different techniques are explored to efficiently compute integral histogram on GPU architecture and applied for fast spatio-temporal median computations and 3D face reconstruction texturing. We proposed a multi-component framework based on semantic fusion of motion information with projected building footprint map to significantly reduce the false alarm rate in urban scenes with many tall structures. The experiments on extensive VOTC2016 benchmark dataset and aerial video confirm that combining complementary tracking cues in an intelligent fusion framework enables persistent tracking for Full Motion Video (FMV) and Wide Aerial Motion Imagery (WAMI).

A robust real-time object detection and tracking system

2008 ◽  
Author(s):  
Zhanfeng Yue ◽  
Pramod Lakshmi Narasimha ◽  
Pankaj Topiwala
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Tracking System ◽  
Object Detection And Tracking ◽  
Detection And Tracking

Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

2011 ◽  
pp. 206-228 ◽  
Author(s):  
Ruth Aguilar-Ponce ◽  
Ashok Kumar ◽  
J. Luis Tecpanecatl-Xihuitl ◽  
Magdy Bayoumi ◽  
Mark Radle
Keyword(s):  
Wireless Sensor Network ◽  
Object Detection ◽  
Sensor Network ◽  
Tracking System ◽  
Visual Surveillance ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Automated Object Detection

The aim of this research was to apply an agent approach to wireless sensor network in order to construct a distributed, automated scene surveillance. Wireless sensor network using visual nodes is used as a framework for developing a scene understanding system to perform smart surveillance. Current methods of visual surveillance depend on highly train personnel to detect suspicious activity. However, the attention of most individuals degrades after 20 minutes of evaluating monitor-screens. Therefore current surveillance systems are prompt to failure. An automated object detection and tracking was developed in order to build a reliable visual surveillance system. Object detection is performed by means of a background subtraction technique known as Wronskian change detection. After discovery, a multi-agent tracking system tracks and follows the movement of each detected object. The proposed system provides a tool to improve the reliability and decrease the cost related to the personnel dedicated to inspect the monitor-screens

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