Characterisation of optical flow anomalies in pedestrian traffic

Andrade, E.L.; Bellows, Scott; Fisher, Robert B.

doi:10.1049/ic:20050073

Cited by 23 publications

(18 citation statements)

References 6 publications

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“…Reference [9] detected the moving vehicles using temporal differencing method, also known as frame differencing method. Reference [12] proposed the framework that detects the changes in the video scenes from optical flow and encodes using hidden Markov model to classify abnormal events.…”

Section: Related Workmentioning

confidence: 99%

Smart Objects Identification System for Robotic Surveillance

Shafie

Ibrahim

Rashid

2014

Int. J. Autom. Comput.

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Video surveillance is an active research topic in computer vision. In this paper, humans and cars identification technique suitable for real time video surveillance systems is presented. The technique we proposed includes background subtraction, foreground segmentation, shadow removal, feature extraction and classification. The feature extraction of the extracted foreground objects is done via a new set of affine moment invariants based on statistics method and these were used to identify human or car. When the partial occlusion occurs, although features of full body cannot be extracted, our proposed technique extracts the features of head shoulder. Our proposed technique can identify human by extracting the human head-shoulder up to 60%-70% occlusion. Thus, it has a better classification to solve the issue of the loss of property arising from human occluded easily in practical applications. The whole system works at approximately 16−29 fps and thus it is suitable for real-time applications. The accuracy for our proposed technique in identifying human is very good, which is 98.33%, while for cars identification, the accuracy is also good, which is 94.41%. The overall accuracy for our proposed technique in identifying human and car is at 98.04%. The experiment results show that this method is effective and has strong robustness.

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Section: Related Workmentioning

confidence: 99%

Smart Objects Identification System for Robotic Surveillance

Shafie

Ibrahim

Rashid

2014

Int. J. Autom. Comput.

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“…Our approach is based on the general concepts in these references and to the best of our knowledge our work is the first combined application of the techniques of optical flow, subspaces and HMMs to assess similarity to the problem of abnormal behaviour detection in crowds. This builds on our previous work in [1] where similar ideas of optical flow similarity based on HMMs were used to analyse variations in the flow patterns of pedestrian traffic. The current work allows for a more flexible model which is in principle able to deal with a large range of people density in the scene from sparse pedestrian traffic to dense crowd flows.…”

Section: Related Workmentioning

confidence: 99%

Detection of emergency events in crowded scenes

Andrade¹,

Fisher²,

Bellows³

2006

IET Conference on Crime and Security

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This paper evaluates a technique for detection of abnormal events in crowds. We characterise crowd behaviour by observing the crowd optical flow and use unsupervised feature extraction to encode normal crowd behaviour. The unsupervised feature extraction applies spectral clustering to find the optimal number of models to represent normal motion patterns. The motion models are HMMs to cope with the variable number of motion samples that might be present in each observation window. The results on simulated crowds analyse the robustness of the approach for detecting crowd emergency scenarios observing the crowd at local and global levels. The results on normal real data show the effectiveness in modelling the more diverse behaviour present in normal crowds. These results improve our previous work in the detection of anomalies in pedestrian data.

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“…This includes finding traffic flow regions, learning the statistics of traffic flow, analyzing traffic flow with prior statistics, and so on. Similar approaches were taken in [7,8] for crowd flow analysis, in that both used optical flow estimation without explicit detection of moving objects. [8] sees human crowd as a set of particles, and this work was further extended to track individuals in high density crowd [9].…”

Section: Introductionmentioning

confidence: 99%

“…[8] sees human crowd as a set of particles, and this work was further extended to track individuals in high density crowd [9]. However, [7] and [8] deal with human crowd scenes which contain less regular flow pattern than traffic scenes. Though a similar approach was taken with traffic scenes [10], our work differs in that our goal is to provide holistic information of a traffic scene, while [10] yields local information of wrong activities inside a scene.…”

Section: Introductionmentioning

confidence: 99%

Estimation and analysis of urban traffic flow

Lee

Bovik

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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This paper describes methods for extracting traffic flow information from urban traffic scenes. The ultimate goal is to collect a macroscopic view of traffic flow information in a fully automatic and segmentation-free way. First, traffic flow is calculated by optical flow estimation. Then, traffic flow regions are defined by the initial traffic flow, and further analysis is performed only in the defined traffic flow regions.Basic statistics of the traffic flow vectors are studied. It is shown that traffic flow computed by optical flow estimation effectively captures traffic scene activity. Also, the statistics of traffic flow vectors contain meaningful and interesting characteristics. An example application demonstrates the applicability and potential uses of the statistics.

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Characterisation of optical flow anomalies in pedestrian traffic

Cited by 23 publications

References 6 publications

Smart Objects Identification System for Robotic Surveillance

Smart Objects Identification System for Robotic Surveillance

Detection of emergency events in crowded scenes

Estimation and analysis of urban traffic flow

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