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
Image: Ravanbakhsh et al
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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.
Ranked #2 on Abnormal Event Detection In Video on UBI-Fights
Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training.
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene.
Ranked #2 on Traffic Accident Detection on A3D
Security surveillance is critical to social harmony and people's peaceful life.
Ranked #3 on Abnormal Event Detection In Video on UBI-Fights
The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
Ranked #1 on Semi-supervised Anomaly Detection on UBI-Fights
ANOMALY DETECTION IN SURVEILLANCE VIDEOS CLASSIFICATION DATA AUGMENTATION SELF-SUPERVISED ANOMALY DETECTION SELF-SUPERVISED LEARNING SEMI-SUPERVISED ANOMALY DETECTION SEMI-SUPERVISED VIDEO CLASSIFICATION
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.
Ranked #1 on Abnormal Event Detection In Video on UBI-Fights
Next, features are extracted from each frame using a convolutional neural network (CNN) that is trained to classify between normal and abnormal frames.
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.