Abstract

Neural network models have revolutionized action recognition in videos, enabling precise and efficient processing of complex visual data. These AI-powered tools mimic or even surpass human abilities in understanding visual information. Convolutional Neural Networks (CNNs) excel at extracting spatial features from individual frames, making them ideal for analyzing intricate scene details. Recurrent Neural Networks (RNNs), particularly LSTMs, add a temporal dimension, capturing movement sequences coherently. To enhance accuracy, Two-Stream Networks were developed, combining static frame analysis with dynamic motion flows to better interpret scenes. Additionally, 3D Convolutional Networks (C3D) treat videos as integrated spatiotemporal units, advancing comprehensive video analysis .These models are widely used in applications like human activity recognition and security surveillance. Their goal is to identify sequential actions, classify them into categories, and map them to predefined event classes. This research examines neural network models for video action recognition, comparing their accuracy, strengths, weaknesses, and proposing improvements. Key findings highlight CNNs’ strength in spatial feature extraction but note limitations in handling temporal dynamics. RNNs and LSTMs address this gap but may struggle with long-term dependencies. Two-Stream Networks and C3D models offer robust solutions by integrating spatial and temporal data but require significant computational resources .Based on testing results, a guidance system is proposed to help users select the most suitable model based on video type. For instance, CNNs are recommended for detailed frame analysis, while LSTMs or C3D models suit videos with complex motion patterns. This approach ensures optimal performance tailored to specific classification needs.