Abnormal Human Activity Recognition using Bayes Classifier and Convolutional Neural Network

This paper introduces a method of abnormal human activity recognition in surveillance video. The method uses Bayes Classifier and Convolutional Neural Network to detect four activities, including walking, running, punching and tripping. KTH dataset is used as the input of Bayes Classifier and Convolutional Neural Network. Moving human target in each frame is detected by Kalman Filter and three features of the target image are extracted. The features include length-width ratio, entropy, and Hu invariant moment. Meanwhile, convolutional neural network of abnormal human activity recognition is built and trained. Experiments show that recognition accuracy of Bayes Classifier reaches 88%, 92%, 92% and 100% for each activity, and Convolutional Neural Network reaches 92%, 96%, 100% and 100% for each activity.