Deep Features Evaluation Method of Human Action Recognition Based on Convolutional Neural Network
TL;DR
A compact and deployable Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) framework that combines a 2D convolutional backbone (AlexNet) for frame-level descriptors with an LSTM head for sequence modeling is proposed, indicating a robust, real-time-capable solution for video understanding in both offline analytics and online deployment.
Abstract
Human Action Recognition (HAR) in unconstrained video remains difficult due to cluttered backgrounds, camera motion, and long-range temporal dependencies. The recognition of human action is the most complex study in the area of Artificial Intelligence (AI) and Computer Vision (CV). Machine vision for online and offline video processing is typically employed in the development of human behavior recognition systems. In video broadcasting and analysis, identifying the type and content of human actions present in the footage is a fundamental requirement. In this article, we propose a compact and deployable Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) framework that combines a 2D convolutional backbone (AlexNet) for frame-level descriptors with an LSTM head for sequence modeling. The approach is evaluated on three representative benchmarks KTH (6 classes), Hollywood-2 (12 actions), and UCF-50 (50 actions). The system attains strong aggregate performance, reaching 98.5% accuracy on KTH, 93.0% accuracy on UCF-50, and 96.0% mAP on Hollywood-2, with improvements distributed broadly across classes. Error–length analyses show that recognition quality rises steadily with longer clips and that the recurrent module extracts the largest gains, while precision–recall and ROC curves with superior Area Under the Curve (AUC) and reliability demonstrate improved ranking and probability calibration across operating points. Despite a larger parameter count than several baselines, the design achieves the most favorable efficiency profile measured at 18ms per frame latency, 135 mJ per frame energy, and the highest throughput placing it on the accuracy–latency–energy Pareto frontier. An ablation study confirms the centrality of temporal modeling (6-12 percentage-point drops without the LSTM), the benefit of longer temporal windows, and the usefulness of stepped learning-rate schedules for stable optimization. The results indicate a robust, real-time-capable solution for video understanding in both offline analytics and online deployment.