Visual Feature Learning on Video Object and Human Action Detection: A Systematic Review

Video object and human action detection are applied in many fields, such as video surveillance, face recognition, etc. Video object detection includes object classification and object location within the frame. Human action recognition is the detection of human actions. Usually, video detection is more challenging than image detection, since video frames are often more blurry than images. Moreover, video detection often has other difficulties, such as video defocus, motion blur, part occlusion, etc. Nowadays, the video detection technology is able to implement real-time detection, or high-accurate detection of blurry video frames. In this paper, various video object and human action detection approaches are reviewed and discussed, many of them have performed state-of-the-art results. We mainly review and discuss the classic video detection methods with supervised learning. In addition, the frequently-used video object detection and human action recognition datasets are reviewed. Finally, a summarization of the video detection is represented, e.g., the video object and human action detection methods could be classified into frame-by-frame (frame-based) detection, extracting-key-frame detection and using-temporal-information detection; the methods of utilizing temporal information of adjacent video frames are mainly the optical flow method, Long Short-Term Memory and convolution among adjacent frames.

Deep Neural Networks for Human Action Recognition

In deep neural networks, human action detection is one of the most demanding and complex tasks. Human gesture recognition is the same as human action recognition. Gesture is defined as a series of bodily motions that communicate a message. Gestures are a more natural and preferable way for humans to engage with computers, thereby bridging the gap between humans and robots. The finest communication platform for the deaf and dumb is human action recognition. We propose in this work to create a system for hand gesture identification that recognizes hand movements, hand characteristics such as peak calculation and angle calculation, and then converts gesture photos into text. Index Terms: Human action recognition, Deaf and dumb, CNN.

Face Recognition and Summarization for Surveillance Video Sequences

Face recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies’ silhouette then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the temporal difference map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training.