Abnormal event detection and localisation in traffic videos based on group sparse topical coding

Ahmadi, Parvin; Tabandeh, Mahmoud; Gholampour, Iman

doi:10.1049/iet-ipr.2015.0399

Cited by 16 publications

(14 citation statements)

References 16 publications

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“…A comparison of our results using the GSTC + dual TV‐L1 and GSTC + Lucas–Kanade algorithms with improved STC with dual‐TVL1 approach [1] and improved GSTC with Lucas–Kanade optical flow [12] is presented in Table 7. Our approach extracts more meaningful traffic motion patterns.…”

Section: Resultsmentioning

confidence: 99%

“…Although the combination of the TM and the dense optical flow yields more motion patterns due to the computing motion vec-tor of each pixel, investigating and obtaining motion vectors of all available pixels impose a high computational cost. On the other hand, the combination of the TM and sparse optical flow has a low computational cost, while it extracts fewer motion FIGURE 13 Some extracted motion patterns using improved GSTC and Lucas-Kanade [12] patterns, and selecting pixels to calculate the motion vector is the challenging task. In other words, pixels displacement between frames as a feature and TM as a learning algorithm are used to extract traffic motion patterns, while it does not need object detection and tracking algorithms.…”

Section: Discussionmentioning

confidence: 99%

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An unsupervised approach for traffic motion patterns extraction

Moradi

Shahbahrami

Akoushideh

2020

IET Image Processing

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Automatic analysis, understanding typical activities, and identifying vehicle behaviour in crowded traffic scenes are fundamental and challenging tasks for traffic video surveillance. Some recent researches have been using machine learning approaches to extract meaningful patterns occurring in a traffic scene, for example, intersection. In this regard, we convert visual patterns and features to visual words using dense and sparse optical flow and learning traffic motion patterns with group sparse topical coding (GSTC) algorithm. In the first step of the proposed algorithm, the input traffic video is divided into non‐overlapping clips. After that, motion vectors are extracted using dual TV‐L1 as a dense optical flow and Lucas–Kanade as a sparse optical flow and converted to flow words. For learning traffic motion patterns, the GSTC algorithm, that is, a non‐probabilistic topic model (TM) has been applied. These patterns represent priors on observable motion, which can be utilised to describe a scene and answer behaviour questions such as what are the motion patterns in a traffic scene and what is going on. The experimental results which have been obtained using a real dataset, QUML, show that the combination of the GSTC + dual TV‐L1 extracts more traffic motion patterns in comparison with the GSTC + Lucas–Kanade and previous studies.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An unsupervised approach for traffic motion patterns extraction

Moradi

Shahbahrami

Akoushideh

2020

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…The detection of both normal (e.g., [1][2][3][4][5][6][7][8][9][10][11][12]) and abnormal (e.g., [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]) video events is a cardinal chore of a surveillance camerasystem. An automated camera system can provide goodtrajectories of objects.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

IJCSE

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Laser scanners have a lot of advantages over conventional video cameras. Data processing in laser scanner system becomes faster and easier because there is no need to record real world videos. Besides, the problem of private life conservation is taken away. This paper proposes an approach to track objects from laser scanned dataset. Data points collected by each scan of laser scanners are considered as video frame. Support vector machine (SVM) along with Hungarian algorithm and Kalman filter are used to obtain trajectories of objects from the laser scanned dataset. Experimental results on the same laser scanned datasetshow that the method of SVM with Hungarian algorithm and Kalman filter performs better than that of its alternative having various thresholds with Hungarian algorithm and Kalman filter.

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“…The visible light camera‐based outdoor long‐range surveillance has many application cases such as the forest fire detection [1] or the wide region security etc. Different from the short range surveillance [2], the long‐range surveillance mainly solves the more‐than‐one‐thousand meters or the farther distance surveillance problem, which means the atmosphere and the environment light will create remarkable influence on the imaging output. The visible light camera can provide the vivid imaging, the intuitional information, and the abundant details; however, it is also notorious for its poor response for the outdoor environment [3, 4].…”

Section: Introductionmentioning

confidence: 99%

Image enhancement for outdoor long‐range surveillance using IQ‐learning multiscale Retinex

Liu

2017

IET Image Processing

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Abnormal event detection and localisation in traffic videos based on group sparse topical coding

Cited by 16 publications

References 16 publications

An unsupervised approach for traffic motion patterns extraction

An unsupervised approach for traffic motion patterns extraction

Untitled

Image enhancement for outdoor long‐range surveillance using IQ‐learning multiscale Retinex

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