Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Abdorreza Alavigharahbagh (Author) Other; Vahid Hajihashemi (Author) Other; José J. M. Machado (Author) FEUP; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Information (Switzerland)

Vol. 14

Pages: 616-616

ISSN: 2078-2489

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

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Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Associated Projects

Sensitive Industry - Sensitive Industry

Other information

Authenticus ID: P-00Z-FBE

DOI: 10.3390/info14110616

Abstract (EN): In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 27