In the field of human interaction recognition, local features based on RGB video often cannot effectively distinguish approximate actions. The Depth image information and the color image information are merged in the recognition process, and a two⁃person interactive behavior recognition algorithm that integrates the depth information and the individual segmentation fusion is proposed.The algorithm firstly extracts the points of interest for RGB and Depth video, then uses 3DSIFT to describe the features on RGB video. The YOLO network is introduced into divide the left and right points of interest on the Depth video, and the local co⁃occurrence matrix is used for local correlation information description. Finally, the nearest neighbor classifier is used to classify the RGB features and Depth features, and further the recognition results are obtained by the decision⁃level fusion, which improves the accuracy of recognition. The results show that the combination of depth visual co⁃occurrence matrix can greatly improve the recognition accuracy of double interaction behavior, and the correct recognition rate of 90% of the actions in SBU Kinect interaction database can verify the effectiveness of the proposed algorithm.

With the popularization of network cameras and the continuous development of image processing technology, visual⁃based fire monitoring systems have received more and more attention. The current method of visual⁃based fire monitoring system is to detect the flame generated after a fire. The disadvantage is that the fire has already occurred during the alarm. The proposed fire detection system firstly finds out the suspected smoke area through the motion detection and the motion characteristics when the smoke just appears, and on this basis, the smoke and flame detection are carried out in the suspected smoke area. The smoke detection combines the smoke color characteristics and texture features. Flame detection uses the combination of flame two color features to improve detection accuracy. The experimental results show that the system can provide an alarm before the fire spreads and accurately detect the location of the fire ignition point, so the effectiveness of the proposed method is verified.