Classification of traffic video based on a spatiotemporal orientation analysis

Derpanis, Konstantinos G.; Wildes, Richard P.

doi:10.1109/wacv.2011.5711560

Cited by 44 publications

(23 citation statements)

References 37 publications

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“…Optical snow arises in many natural situations where the imaged scene elements are restricted to a single direction of motion but vary in speed (e.g., camera translating across a static scene containing a range of depths and vehicular traffic scenes [45], where the speeds may vary but the direction of motion is generally uniform). In the frequency domain, the energy approximately corresponds to a "bow tie" signature formed by the superposition of planes.…”

Section: Orientation In Visual Spacetimementioning

confidence: 99%

Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis

Derpanis

Wildes

2012

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-This paper is concerned with the representation and recognition of the observed dynamics (i.e., excluding purely spatial appearance cues) of spacetime texture based on a spatiotemporal orientation analysis. The term "spacetime texture" is taken to refer to patterns in visual spacetime, (x, y, t), that primarily are characterized by the aggregate dynamic properties of elements or local measurements accumulated over a region of spatiotemporal support, rather than in terms of the dynamics of individual constituents. Examples include image sequences of natural processes that exhibit stochastic dynamics (e.g., fire, water and windblown vegetation) as well as images of simpler dynamics when analyzed in terms of aggregate region properties (e.g., uniform motion of elements in imagery, such as pedestrians and vehicular traffic). Spacetime texture representation and recognition is important as it provides an early means of capturing the structure of an ensuing image stream in a meaningful fashion. Toward such ends, a novel approach to spacetime texture representation and an associated recognition method are described based on distributions (histograms) of spacetime orientation structure. Empirical evaluation on both standard and original image data sets show the promise of the approach, including significant improvement over alternative state-of-the-art approaches in recognizing the same pattern from different viewpoints.

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Section: Orientation In Visual Spacetimementioning

confidence: 99%

Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis

Derpanis

Wildes

2012

IEEE Trans. Pattern Anal. Mach. Intell.

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“…As the focus of this paper is surveillance applications, the tracker is evaluated on several surveillance datasets with stationary cameras including CAVIAR 4 , PETS2007 5 , i-LIDS 6 and York dataset 7 . In the following text, we refer to our proposed tracker as IMSOE, as it works based on 'Intensity' and 'MSOE' features.…”

Section: Methodsmentioning

confidence: 99%

“…The SOE feature set is an integrated and modern framework, proposed for analysis of dynamic patterns based on their constituent space-time orientation structure in the video. This framework has been successfully applied in various computer vision applications such as 'Dynamic Texture Recognition and Scene Understanding', 'Action Recognition' and 'Visual Tracking', to name a few [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Novelty detection in human tracking based on spatiotemporal oriented energies

Emami

Harandi

Dadgostar

et al. 2015

Pattern Recognition

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Integrated analysis of spatial and temporal domains are considered to overcome some of the challenging computer vision problems such as 'Dynamic Scene Understanding' and 'Action Recognition'. In visual tracking, 'Spatiotemporal Oriented Energy' (SOE) features are successfully applied to locate the object in cluttered scenes under varying illumination. In contrast to previous studies, this paper introduces SOE features for occlusion modeling and novelty detection in tracking. To this end, we propose a Bayesian state machine that exploits SOE information to analyze occlusion and identify the target status in the course of tracking. The proposed approach can be seamlessly merged with a generic tracking system to prevent template corruption (for example when the target is occluded). Comparative evaluations show that the proposed approach could significantly improve the performance of a generic tracking system in challenging occlusion situations.

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“…Henceforth they demonstrated the strength of their framework in various computer vision applications [9,7]. We refer to this framework as 'Marginalised SOE' (MSOE) in this paper, which will be discussed in detail.…”

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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Classification of traffic video based on a spatiotemporal orientation analysis

Cited by 44 publications

References 37 publications

Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis

Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis

Novelty detection in human tracking based on spatiotemporal oriented energies

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

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