Learning Multimodal Deep Representations for Crowd Anomaly Event Detection
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Summary
In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes using low-level visual features, energy features, and motion map features simultaneously extracted based on spatiotemporal energy measurements.
- Type
- article
- Published
- 2018-01-31
- Cited by
- 25
- References
- 38
- Access
- Open access
- OpenAlex
- https://openalex.org/W2785768523
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:125960903
Keywords
Anomaly detection, Artificial intelligence, Computer science, Boltzmann machine, Representation (politics)
References
- Deep Boltzmann Machines
- PCANet: A Simple Deep Learning Baseline for Image Classification?
- Recognize complex events from static images by fusing deep channels
- Real-time anomaly detection and localization in crowded scenes
- Anomaly Detection and Localization in Crowded Scenes
- Unsupervised learning of hierarchical representations with convolutional deep belief networks
- Abnormal event detection in crowded scenes based on Structural Multi-scale Motion Interrelated Patterns
- Abnormal detection using interaction energy potentials
- Sparse reconstruction cost for abnormal event detection
- Online detection of unusual events in videos via dynamic sparse coding
- Learning Hierarchical Features for Scene Labeling
- Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Spatiotemporal energy models for the perception of motion.
- Abnormal event detection in crowded scenes using sparse representation
- Trajectory-Based Anomalous Event Detection
- Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis
- A Review of Anomaly Detection in Automated Surveillance
- Learning a Deep Compact Image Representation for Visual Tracking
- Anomaly detection in crowded scenes
Cited by
- Graph-based Correlated Topic Model for Motion Patterns Analysis in Crowded Scenes from Tracklets
- Transfer learning for video anomaly detection
- A very deep two-stream network for crowd type recognition
- Unsupervised deep learning system for local anomaly event detection in crowded scenes
- 3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance
- Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning
- Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects
- Computer Vision and Image Processing: 4th International Conference, CVIP 2019, Jaipur, India, September 27–29, 2019, Revised Selected Papers, Part II
- Motion-shape-based deep learning approach for divergence behavior detection in high-density crowd
- PANDA: Perceptually Aware Neural Detection of Anomalies
- Comparative Performance Analysis of Optical Flow Algorithms for Anomaly Detection
- Human Anomaly Detection in Surveillance Videos: A Review
- Unusual Activity Detection in Surveillance Video Scene: Review
- Crowd abnormality detection in video sequences using supervised convolutional neural network
- A novel framework for detection of motion and appearance-based Anomaly using ensemble learning and LSTMs
- MuST-POS: multiscale spatial-temporal 3D atrous-net and PCA guided OC-SVM for crowd panic detection
- TS-MDA: two-stream multiscale deep architecture for crowd behavior prediction
- A Novel Deep Architecture for Multi-Task Crowd Analysis
- A Review on Methods and Applications in Multimodal Deep Learning
- Analysis of Video Forensics System for Detection of Gun, Mask and Anomaly Using Soft Computing Techniques
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