Crowd Recognition System Based on Optical Flow Along with SVM classifier

The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity.

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

Video trajectory analysis using unsupervised clustering and multi-criteria ranking

Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead

Modeling Trajectory-level Behaviors using Time Varying Pedestrian Movement Dynamics

We present a novel interactive multi-agent simulation algorithm to model pedestrian movement dynamics. We use statistical techniques to compute the movement patterns and motion dynamics from 2D trajectories extracted from crowd videos. Our formulation extracts the dynamic behavior features of real-world agents and uses them to learn movement characteristics on the fly. The learned behaviors are used to generate plausible trajectories of virtual agents as well as for long-term pedestrian trajectory prediction. Our approach can be integrated with any trajectory extraction method, including manual tracking, sensors, and online tracking methods. We highlight the benefits of our approach on many indoor and outdoor scenarios with noisy, sparsely sampled trajectory in terms of trajectory prediction and data-driven pedestrian simulation

Deep Learning for Crowd Anomaly Detection

Today, public areas across the globe are monitored by an increasing amount of surveillance cameras. This widespread usage has presented an ever-growing volume of data that cannot realistically be examined in real-time. Therefore, efforts to understand crowd dynamics have brought light to automatic systems for the detection of anomalies in crowds. This thesis explores the methods used across literature for this purpose, with a focus on those fusing dense optical flow in a feature extraction stage to the crowd anomaly detection problem. To this extent, five different deep learning architectures are trained using optical flow maps estimated by three deep learning-based techniques. More specifically, a 2D convolutional network, a 3D convolutional network, and LSTM-based convolutional recurrent network, a pre-trained variant of the latter, and a ConvLSTM-based autoencoder is trained using both regular frames and optical flow maps estimated by LiteFlowNet3, RAFT, and GMA on the UCSD Pedestrian 1 dataset. The experimental results have shown that while prone to overfitting, the use of optical flow maps may improve the performance of supervised spatio-temporal architectures