This video will teach you -
What is Anomaly detection?
How Anomaly detection algorithm work?
Implementation of Anomaly detection code of this paper: arxiv.org/pdf/1801.04264v3
GitHub: github.com/AarohiSingla/Anoma...
Email id : aarohisingla1987@gmail.com
The paper proposes a method for learning anomalies in surveillance videos without the need for annotating anomalous segments, which can be ti me-consuming. Instead, it suggests leveraging weakly labeled training videos, where the labels (anomalous or normal) are assigned at the video level rather than the clip level.
The paper introduces a new large-scale dataset consisting of 128 hours of real-world surveillance videos, containing various anomalies such as fighting, road accidents, burglary, robbery, etc., along with normal activities. This dataset serves two purposes: general anomaly detection considering all anomalies as one group and all normal activities as another group, and recognizing each of the 13 anomalous activities individually.
Goal:
The ultimate goal of the proposed system is to enhance the efficiency of video surveillance by automatically detecting anomalous events, such as traffic accidents or crimes, without the need for extensive human monitoring.
This contributes to public safety by enabling timely detection and response to unusual activities captured by surveillance cameras.
Proposed Anomaly Detection Method:
The approach treats normal and anomalous videos as "bags" and video segments as "instances"
The proposed approach begins with dividing surveillance videos into a fixed number of segments during training. These segments make instances in a bag. Using both positive (anomalous) and negative (normal) bags, we train the anomaly detection model using the proposed deep MIL ranking loss.
Proposed dataset:
the paper introduces a new large-scale dataset consisting of 128 hours of real-world surveillance videos, containing various anomalies such as fighting, road accidents, burglary, robbery, etc., along with normal activities. This dataset serves two purposes: general anomaly detection considering all anomalies as one group and all normal activities as another group, and recognizing each of the 13 anomalous activities individually.
We divide our dataset into two parts: the training set consisting of 800 normal and 810 anomalous videos (details shown in Table 2) and the testing set including the remaining 150 normal and 140 anomalous videos. Both training and testing sets contain all 13 anomalies at various temporal locations in the videos. Furthermore, some of the videos have multiple anomalies.
#computervision #anomaly #anomalydetection #artificialintelligence #deeplearning
15 май 2024
