Application of Temporal Action Detection Technology in Abnormal Event Detection of Surveillance Video

By detecting abnormal violation event in surveillance videos, the safety management capabilities in high-risk power operations can be improved. This research constructs an intelligent abnormal event detection technology using deep learning algorithms, aiming to improve the detection accuracy of anomaly event. This research improves the parameter setting method and fully connected layer of three-dimensional convolutional networks to enhance their ability to recognize three-dimensional features. An improved algorithm is adopted as the basic structure of temporal action detection technology. Frame interpolation is applied to improve the accuracy of temporal action detection. A monitoring video anomaly event detection model based on the improved temporal action detection technology is established. The experiment outcomes show that the improved three-dimensional convolutional network achieves convergence after 32 iterations, with an accuracy of 99.15% and a recall rate of 98.3%. The average accuracy of the three datasets tested is better than other algorithms. The average precision of the research model for detecting throwing objects from high altitude, crossing fences, smoking, and checking electricity without gloves are 89.1%, 88.9%, 96.6%, and 96.2%, respectively. The accuracy for abnormal event detection of different time periods is superior to other models. The average recall value of the research model is 94.3%, which is higher than other models. The results indicate that the research model has the capacity to accurately recognize abnormal events in massive, diverse, and complex surveillance videos. The abnormal event detection model proposed in the study can be applied to the intelligent management platform of the power industry, thereby improving the safety management capability in power operations.