Enhancing Human Action Recognition through advanced Motion Descriptors and Machine Learning

Understanding human activities from videos presents a formidable challenge in Computer Vision. Intelligent video systems primarily specialize in automatically recognizing human actions within video sequences and annotating them accordingly; human action identification involves recognizing actions associated with one or more objects based on analysis of their activities and context. As technology has proliferated rapidly, huge amounts of human-centric video data have accumulated; it has become an obstacle in contemporary video processing systems due to identifying all these actions efficiently. Complex backgrounds compound these challenges, creating error margins that severely hamper many object detection algorithms and creating error margins that compromise performance - particularly with regard to occlusion and distortion issues. This work addresses these obstacles using Apache Spark with in-memory computing and distributed environment for improved recognition of human actions; specifically, a Local Motion Descriptor is employed for feature extraction that calculates intensities between frames that include both motion and appearance elements. Spark MLlib employs random forest to effectively recognize human behavior. Experimental studies are performed to evaluate its activity recognition capabilities and results are benchmarked against popular supervised classification techniques like Decision Trees; results demonstrate that our proposed model outshone other classification techniques, confirming its efficacy in activity recognition.