Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis

Albattah

et al. 2020

Sensors

Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

Section: Approachesmentioning

confidence: 99%

Advances and Trends in Real Time Visual Crowd Analysis

Albattah

et al. 2020

Sensors

“…Local anomalies usually focus on pedestrians or vehicles that are different from most pedestrians in the scene. For example, the local abnormal behavior is detected by extracting the track of pedestrians in [ 42 ], and the anomaly in the scene is located by analyzing the texture features in the image in [ 43 ].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Evaluation of Crowd Safety Based on Pedestrians’ Number and Distribution Entropy

Zhang

Wang

2020

Entropy

Crowd video monitoring and analysis is a hot topic in computer vision and public management. The pre-evaluation of crowd safety is beneficial to the prediction of crowd status to avoid the occurrence of catastrophic events. This paper proposes a method to evaluate crowd safety based on fuzzy inference. Pedestrian’s number and distribution uniformity are considered in a fuzzy inference system as two kinds of attributes of a crowd. Firstly, the pedestrian’s number is estimated by the number of foreground pixels. Then, the distribution uniformity of a crowd is calculated using distribution entropy by dividing the monitoring scene into several small areas. Furthermore, through the fuzzy operation, the fuzzy system is constructed by using two input variables (pedestrian’s number and distribution entropy) and one output variable (crowd safety status). Finally, inference rules between the crowd safety state and the pedestrian’s number and distribution uniformity are constructed to obtain the pre-evaluation of the safety state of the crowd. Three video sequences extracted from different scenes are used in the experiment. Experimental results show that the proposed method can be used to evaluate the safety status of the crowd in a monitoring scene.

“…A motion vector field is thus established. In this paper, considering the spatio-temporal information in motion detection [ 11 ], we adopt an improved algorithm based on Lucas-Kanade optical flow algorithm [ 12 ] for this task, namely pyramid optical flow algorithm [ 13 ].…”

Section: Calculation Of Crowd Velocity Vector Fieldmentioning

confidence: 99%

Detection of Salient Crowd Motion Based on Repulsive Force Network and Direction Entropy

Zhang

Lin

Zheng³

et al. 2019

Entropy

This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network using the repulsive force. The interaction force between two particles calculated by the repulsive force formula is used to determine the relationship between these two particles. The network node strength is used as a feature parameter to construct a two-dimensional feature matrix. Furthermore, the entropy of the velocity vector direction is calculated to describe the instability of the crowd movement. Finally, the feature matrix of the repulsive force network and direction entropy are integrated together to detect the salient crowd motion. Experimental results and comparison show that the proposed method can efficiently detect the salient crowd motion.