Classification of Moving Targets Based on Motion and Appearance

Bogomolov, Yuri; Dror, Gideon; Lapchev, Stanislav; Rivlin, Ehud; Rudzsky, Michael

doi:10.5244/c.17.44

Cited by 29 publications

(15 citation statements)

References 27 publications

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“…Various feature sets have been utilised for vision-based tracking, including intensity and colour [12,13], histogram of gradients and motion cues [3]. In an attempt to overcome some of the wellknown challenges in this area, integrated analysis on spatial and temporal domains have been considered in the last two decades.…”

Section: Introductionmentioning

confidence: 99%

Role of Spatiotemporal Oriented Energy Features for Robust Visual Tracking in Video Surveillance

Emami

Dadgostar

Bigdeli

et al. 2012

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance

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We propose an effective approach to take advantage of the rich description provided by Spatiotemporal Oriented Energy features for the purpose of robust tracking. There are two core components in our system. The first one is a compound measure of 'Coherent Motion' and 'Identity Motion Signature' which is introduced based on motion dynamics of the targets. This measure is used for robust optimisation in occluded situations as well as an adaptive template updating scheme. The second component is a state machine which detects various states of the targets based on statistical analysis of their 'Motion Signature'. Empirical evaluations demonstrate improvement in performance of the tracking system along with the role of each component.

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Section: Introductionmentioning

confidence: 99%

Role of Spatiotemporal Oriented Energy Features for Robust Visual Tracking in Video Surveillance

Emami

Dadgostar

Bigdeli

et al. 2012

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance

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“…Thus, moving objects can be classified as single person, group of persons or vehicle based on their RMI classifier. Another is a hybrid classification system [7] can be used to recognize moving objects based on motion and appearance features simultaneously. Support vector machine (SVM) as a classifier [8] is used for classification of moving object.…”

Section: Introductionmentioning

confidence: 99%

Neural Network based Approach for Recognition Human Motion using Stationary Camera

Modi¹,

Mehta²

2011

IJCA

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Video surveillance is currently one of the most active research topics in the computer vision community. During motion, the surveillance system can detect moving objects and identify them as humans, animals, vehicles. This strong interest is driven by a wide spectrum of promising applications in surveillance system such as Military security, Public and commercial security, etc. The model includes detection, feature extraction and recognition of people from image sequences involving humans. In proposed system frame differencing and Neural Network is used for moving object detection and recognition of human motion respectively. Experimental results show that human motion can be correctly classified. General TermsMotion detection and recognition

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“…Extensive research efforts have been dedicated to moving object recognition, where many approaches, such as [1] - [5] have been presented to tackle this problem.…”

Section: Introductionmentioning

confidence: 99%