scholarly journals Optical Flow-Based Weighted Magnitude and Direction Histograms for the Detection of Abnormal Visual Events Using Combined Classifier

2021 ◽  
Vol 15 (3) ◽  
pp. 12-30
Author(s):  
Gajendra Singh ◽  
Rajiv Kapoor ◽  
Arun Khosla
Keyword(s):  
Optical Flow ◽  
Flow Direction ◽  
Motion Pattern ◽  
Local Motion ◽  
Visual Events ◽  
Motion Features ◽  
Crowd Motion ◽  
Combined Classifier ◽  
Abnormal Visual ◽  
Crowded Scenes

Movement information of persons is a very vital feature for abnormality detection in crowded scenes. In this paper, a new method for detection of crowd escape event in video surveillance system is proposed. The proposed method detects abnormalities based on crowd motion pattern, considering both crowd motion magnitude and direction. Motion features are described by weighted-oriented histogram of optical flow magnitude (WOHOFM) and weighted-oriented histogram of optical flow direction (WOHOFD), which describes local motion pattern. The proposed method uses semi-supervised learning approach using combined classifier (KNN and K-Means) framework to detect abnormalities in motion pattern. The authors validate the effectiveness of the proposed approach on publicly available UMN, PETS2009, and Avanue datasets consisting of events like gathering, splitting, and running. The technique reported here has been found to outperform the recent findings reported in the literature.

scholarly journals A Driver’s Visual Attention Prediction Using Optical Flow

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3722
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Visual Attention ◽  
Optical Flow ◽  
Prediction Models ◽  
Apparent Movement ◽  
Image Sequences ◽  
Motion Information ◽  
Accuracy Improvement ◽  
Flow Maps ◽  
Current State ◽  
Motion Features

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.

scholarly journals A System for Movement Detecting Congestion.

2014 ◽  
Vol 13 (3) ◽  
pp. 4302-4307
Author(s):  
Reeja S. R ◽  
Dr. N. P. Kavya
Keyword(s):  
Optical Flow ◽  
Optic Flow ◽  
Video Segmentation ◽  
Video Sequences ◽  
Protective Measures ◽  
Flow Computation ◽  
Congested Area ◽  
Crowd Motion ◽  
Flow Vectors ◽  
Early Information

In this paper, we present a system for tracking and provide early information of hazardous locationsin huge gatherings. It is based on optic flow estimations and detects sequences of crowd motion that are characteristic for devastating congestions. For optic flow computation, Lucas- Kanade method is employed to determine the optical flow vectors for the gathered video. Segmentation of video sequences is done and optic flow is determined for respective segments. A threshold optic flow is chosen in such a way that the tracking of congested area in video is easilydoneby comparing it with respective segment’s determined optic flow values. Finally, we present the location of crowd congestion which helps in taking further protective measures to handle unusual events.  

Motion Features for Visual Speech Recognition

2009 ◽  
pp. 388-415 ◽  
Author(s):  
Wai Chee Yau ◽  
Dinesh Kant Kumar ◽  
Hans Weghorn
Keyword(s):  
Speech Recognition ◽  
Optical Flow ◽  
Visual Speech ◽  
Image Subtraction ◽  
Phoneme Classification ◽  
Visual Speech Recognition ◽  
Lip Reading ◽  
Motion Features ◽  
Spatio Temporal ◽  
Mouth Movement

The performance of a visual speech recognition technique is greatly influenced by the choice of visual speech features. Speech information in the visual domain can be generally categorized into static (mouth appearance) and motion (mouth movement) features. This chapter reviews a number of computer-based lip-reading approaches using motion features. The motion-based visual speech recognition techniques can be broadly categorized into two types of algorithms: optical-flow and image subtraction. Image subtraction techniques have been demonstrated to outperform optical-flow based methods in lip-reading. The problem with image subtraction-based method using difference of frames (DOF) is that these features capture the changes in the images over time, but do not indicate the direction of the mouth movement. New motion features to overcome the limitation of the conventional image subtraction-based techniques in visual speech recognition are presented in this chapter. The proposed approach extracts features by applying motion segmentation on image sequences. Video data are represented in a 2-D space using grayscale images named as motion history images (MHI). MHIs are spatio-temporal templates that implicitly encode the temporal component of mouth movement. Zernike moments are computed from MHIs as image descriptors and classified using support vector machines (SVMs). Experimental results demonstrate that the proposed technique yield a high accuracy in a phoneme classification task. The results suggest that dynamic information is important for visual speech recognition.

scholarly journals Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes

2015 ◽  
Vol 11 (11) ◽  
pp. 406941 ◽  
Author(s):  
Ang Li ◽  
Zhenjiang Miao ◽  
Yigang Cen ◽  
Tian Wang ◽  
Viacheslav Voronin
Keyword(s):  
Optical Flow ◽  
Events Detection ◽  
Crowded Scenes ◽  
Abnormal Events

scholarly journals Computing Optical Flow in the Primate Visual System

1989 ◽  
Vol 1 (1) ◽  
pp. 92-103 ◽  
Author(s):  
H. Taichi Wang ◽  
Bimal Mathur ◽  
Christof Koch
Keyword(s):  
Visual System ◽  
Optical Flow ◽  
Difficult Problem ◽  
Population Coding ◽  
Spatial Integration ◽  
Local Motion ◽  
Temporal Area ◽  
Second Stage ◽  
Vector Sum ◽  
Cooperative Algorithm

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We show how gradient models, a well-known class of motion algorithms, can be implemented within the magnocellular pathway of the primate's visual system. Our cooperative algorithm computes optical flow in two steps. In the first stage, assumed to be located in primary visual cortex, local motion is measured while spatial integration occurs in the second stage, assumed to be located in the middle temporal area (MT). The final optical flow is extracted in this second stage using population coding, such that the velocity is represented by the vector sum of neurons coding for motion in different directions. Our theory, relating the single-cell to the perceptual level, accounts for a number of psychophysical and electrophysiological observations and illusions.

scholarly journals Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Shaonian Huang ◽  
Dongjun Huang ◽  
Mansoor Ahmed Khuhro
Keyword(s):  
Motion Analysis ◽  
Video Surveillance ◽  
Latent Dirichlet Allocation ◽  
Abnormal Behavior ◽  
Surveillance Video ◽  
Social Force ◽  
Local Motion ◽  
Allocation Model ◽  
Abnormal Behavior Detection ◽  
Crowd Motion

Over the past decades, crowd management has attracted a great deal of attention in the area of video surveillance. Among various tasks of video surveillance analysis, crowd motion analysis is the basis of numerous subsequent applications of surveillance video. In this paper, a novel social force graph with streak flow attribute is proposed to capture the global spatiotemporal changes and the local motion of crowd video. Crowd motion analysis is hereby implemented based on the characteristics of social force graph. First, the streak flow of crowd sequence is extracted to represent the global crowd motion; after that, spatiotemporal analogous patches are obtained based on the crowd visual features. A weighted social force graph is then constructed based on multiple social properties of crowd video. The graph is segmented into particle groups to represent the similar motion patterns of crowd video. A codebook is then constructed by clustering all local particle groups, and consequently crowd abnormal behaviors are detected by using the Latent Dirichlet Allocation model. Extensive experiments on challenging datasets show that the proposed method achieves preferable results in the application of crowd motion segmentation and abnormal behavior detection.

scholarly journals Computing motion in the primate's visual system

1989 ◽  
Vol 146 (1) ◽  
pp. 115-139
Author(s):  
C. Koch ◽  
H. T. Wang ◽  
B. Mathur
Keyword(s):  
Flow Field ◽  
Visual System ◽  
Optical Flow ◽  
Visual Motion ◽  
Population Coding ◽  
Spatial Integration ◽  
Local Motion ◽  
Temporal Area ◽  
Second Stage ◽  
Optical Flow Field

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We will show how one well-known gradient-based computer algorithm for estimating visual motion can be implemented within the primate's visual system. This relaxation algorithm computes the optical flow field by minimizing a variational functional of a form commonly encountered in early vision, and is performed in two steps. In the first stage, local motion is computed, while in the second stage spatial integration occurs. Neurons in the second stage represent the optical flow field via a population-coding scheme, such that the vector sum of all neurons at each location codes for the direction and magnitude of the velocity at that location. The resulting network maps onto the magnocellular pathway of the primate visual system, in particular onto cells in the primary visual cortex (V1) as well as onto cells in the middle temporal area (MT). Our algorithm mimics a number of psychophysical phenomena and illusions (perception of coherent plaids, motion capture, motion coherence) as well as electrophysiological recordings. Thus, a single unifying principle ‘the final optical flow should be as smooth as possible’ (except at isolated motion discontinuities) explains a large number of phenomena and links single-cell behavior with perception and computational theory.

scholarly journals Modeling Pedestrian Motion in Crowded Scenes Based on the Shortest Path Principle

2021 ◽  
Vol 12 (1) ◽  
pp. 381
Author(s):  
Yi Zou ◽  
Yuncai Liu
Keyword(s):  
Shortest Path ◽  
State Of The Art ◽  
Meaningful Work ◽  
Movement Behavior ◽  
Motion Model ◽  
Human Dynamics ◽  
Motion Trajectories ◽  
Crowd Motion ◽  
Crowded Scenes ◽  
Part Motion

In the computer vision field, understanding human dynamics is not only a great challenge but also very meaningful work, which plays an indispensable role in public safety. Despite the complexity of human dynamics, physicists have found that pedestrian motion in a crowd is governed by some internal rules, which can be formulated as a motion model, and an effective model is of great importance for understanding and reconstructing human dynamics in various scenes. In this paper, we revisit the related research in social psychology and propose a two-part motion model based on the shortest path principle. One part of the model seeks the origin and destination of a pedestrian, and the other part generates the movement path of the pedestrian. With the proposed motion model, we simulated the movement behavior of pedestrians and classified them into various patterns. We next reconstructed the crowd motions in a real-world scene. In addition, to evaluate the effectiveness of the model in crowd motion simulations, we created a new indicator to quantitatively measure the correlation between two groups of crowd motion trajectories. The experimental results show that our motion model outperformed the state-of-the-art model in the above applications.

scholarly journals Motion-Induced Scotoma

Perception ◽  
2019 ◽  
Vol 48 (2) ◽  
pp. 115-137 ◽  
Author(s):  
Tatjana Seizova-Cajic ◽  
Nika Adamian ◽  
Marianne Duyck ◽  
Patrick Cavanagh
Keyword(s):  
Visual Angle ◽  
Prolonged Exposure ◽  
Receptive Fields ◽  
Motion Pattern ◽  
Local Motion ◽  
Motion Trajectories ◽  
Smooth Motion ◽  
Sensory Modalities ◽  
Sensory Maps ◽  
Motion Detectors

We investigated artificial scotomas created when a moving object instantaneously crossed a gap, jumping ahead and continuing its otherwise smooth motion. Gaps of up to 5.1 degrees of visual angle, presented at 18° eccentricity, either closed completely or appeared much shorter than when the same gap was crossed by two-point apparent motion, or crossed more slowly, mimicking occlusion. Prolonged exposure to motion trajectories with a gap in most cases led to further shrinking of the gap. The same gap-shrinking effect has previously been observed in touch. In both sensory modalities, it implicates facilitation among codirectional local motion detectors and motion neurons with receptive fields larger than the gap. Unlike stimuli that simply deprive a receptor surface of input, suggesting it is insentient, our motion pattern skips a section in a manner that suggests a portion of the receptor surface has been excised, and the remaining portions stitched back together. This makes it a potentially useful tool in the experimental study of plasticity in sensory maps.

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