Abnormal Event Detection In Video
12 papers with code • 2 benchmarks • 4 datasets
Abnormal Event Detection In Video is a challenging task in computer vision, as the definition of what an abnormal event looks like depends very much on the context. For instance, a car driving by on the street is regarded as a normal event, but if the car enters a pedestrian area, this is regarded as an abnormal event. A person running on a sports court (normal event) versus running outside from a bank (abnormal event) is another example. Although what is considered abnormal depends on the context, we can generally agree that abnormal events should be unexpected events that occur less often than familiar (normal) events
Source: Unmasking the abnormal events in video
Image: Ravanbakhsh et al
Latest papers
Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection
Surprisingly, we find that this simple representation is sufficient to achieve state-of-the-art performance in ShanghaiTech, the largest and most complex VAD dataset.
Normalizing Flows for Human Pose Anomaly Detection
Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more.
Iterative weak/self-supervised classification framework for abnormal events detection
The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events.
Weakly and Partially Supervised Learning Frameworks for Anomaly Detection
The main objective is to provide several solutions to the mentioned problems, by focusing on analyzing previous state-of-the-art methods and presenting an extensive overview to clarify the concepts employed on capturing normal and abnormal patterns.
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video
Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training.
Generative Neural Networks for Anomaly Detection in Crowded Scenes
Security surveillance is critical to social harmony and people's peaceful life.
Real-world Anomaly Detection in Surveillance Videos
To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Abnormal event detection on BMTT-PETS 2017 surveillance challenge
Next, features are extracted from each frame using a convolutional neural network (CNN) that is trained to classify between normal and abnormal frames.