Action Recognition with Neural Networks

With the increasing usage of monitoring applications such as environments, entertainment, and healthcare systems, “Activity Recognition” has gained a lot of attention in video analytics technology. If we consider large-scale surveillance systems, such as finding suspicious activity in an airport, it is also a challenging task for trained human resources. If we can't find the culprit, it results in adverse situations. Considering the healthcare system, observing patients all day long is one such task. If we detect suspicious activity, it means we can respond spontaneously. This is how activity recognition can be applied in real-world scenarios. Gesture recognition and prediction are closely related to most computer vision problems, such as human gesture prediction and recognition, gait recognition, and future event recognition. Enterprise products like Microsoft's Kinect are notable examples of action recognition techniques. Most surveillance systems attempt to solve the problem of video stream/file input-based detection, prediction, and activity recognition of humans and vehicles for AI-based surveillance of social environments such as apartments and public environments such as busy road crossings, railway stations, and airports.