Crowd Motion Monitoring with Thermodynamics-Inspired Feature
نویسندگان
چکیده
Crowd motion in surveillance videos is comparable to heat motion of basic particles. Inspired by that, we introduce Boltzmann Entropy to measure crowd motion in optical flow field so as to detect abnormal collective behaviors. As a result, the collective crowd moving pattern can be represented as a time series. We found that when most people behave anomaly, the entropy value will increase drastically. Thus, a threshold can be applied to the time series to identify abnormal crowd commotion in a simple and efficient manner without machine learning. The experimental results show promising performance compared with the state of the art methods. The system works in real time with high precision.
منابع مشابه
Towards crowd density-aware video surveillance applications
Crowd density analysis is a crucial component in visual surveillance mainly for security monitoring. This paper proposes a novel approach for crowd density measure, in which local information at pixel level substitutes a global crowd level or a number of people per-frame. The proposed approach consists of generating automatic crowd density maps using local features as an observation of a probab...
متن کاملA Bio-Inspired, Motion-Based Analysis of Crowd Behavior Attributes Relevance to Motion Transparency, Velocity Gradients, and Motion Patterns
The analysis of motion crowds is concerned with the detection of potential hazards for individuals of the crowd. Existing methods analyze the statistics of pixel motion to classify non-dangerous or dangerous behavior, to detect outlier motions, or to estimate the mean throughput of people for an image region. We suggest a biologically inspired model for the analysis of motion crowds that extrac...
متن کاملCrowd Emotion Detection Using Dynamic Probabilistic Models
Detecting emotions of a crowd to control the situation is an area of emerging interest. The purpose of this paper is to present a novel idea to detect the emotions of the crowd. Emotions are defined as evolving quantities arising from the reaction to contextual situations in a set of dynamic pattern of events. These events depend on internal and external interaction states in an already mapped ...
متن کاملAbnormal Crowd Motion Detection with Hidden Conditional Random Fields Model
Crowd motion analysis in public places is an important research subject in the monitoring field. This paper proposes an approach for detecting abnormal crowd motion using Hidden Conditional Random Fields Model (HCRF). This approach derives variations of motion patterns from direction distribution of the crowd motion obtained by the optical flow and these variations are encoded with HCRF to allo...
متن کاملPerformance Analysis of Event Detection Models in Crowded Scenes
This paper evaluates an automatic technique for detection of abnormal events in crowds. Crowd behaviour is difficult to predict and might not be easily semantically translated. Moreover it is difficult to track individuals in the crowd using state of the art tracking algorithms. Therefore we characterise crowd behaviour by observing the crowd optical flow and use unsupervised feature extraction...
متن کامل