Nada Alzahrani
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Nada Alzahrani has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
Ghada Badr
King Saud University
1 shared publication- Mohamed Maher Ben Ismail
Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
1 shared publication Nourah Alaseem
King Saud University
1 shared publication- Ouiem Bchir
Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
1 shared publication Rasha Alsulim
King Saud University
1 shared publication
Latest Publications
- Open Access
YOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences
Automated action recognition has become essential in the surveillance, healthcare, and multimedia retrieval industries owing to the rapid proliferation of video data. This paper introduces YOLO-Act, a novel spatiotemporal action detection model that enhances the object detection capabilities of YOLOv8 to efficiently manage complex action dynamics within video sequences. YOLO-Act achieves precise and efficient action recognition by integrating keyframe extraction, action tracking, and class fusion. The model depicts essential temporal dynamics without the computational overhead of continuous frame processing by leveraging the adaptive selection of three keyframes representing the beginning, middle, and end of the actions. Compared with state-of-the-art approaches such as the Lagrangian Action Recognition Transformer (LART), YOLO-Act exhibits superior performance with a mean average precision (mAP) of 73.28 in experiments conducted on the AVA dataset, resulting in a gain of +28.18 mAP. Furthermore, YOLO-Act achieves this higher accuracy with significantly lower FLOPs, demonstrating its efficiency in computational resource utilization. The results highlight the advantages of incorporating precise tracking, effective spatial detection, and temporal consistency to address the challenges associated with video-based action detection.
Open AccessYOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences
Automated action recognition has become essential in the surveillance, healthcare, and multimedia retrieval industries owing to the rapid proliferation of video data. This paper introduces YOLO-Act, a novel spatiotemporal action detection model that enhances the object detection capabilities of YOLOv8 to efficiently manage complex action dynamics within video sequences. YOLO-Act achieves precise and efficient action recognition by integrating keyframe extraction, action tracking, and class fusion. The model depicts essential temporal dynamics without the computational overhead of continuous frame processing by leveraging the adaptive selection of three keyframes representing the beginning, middle, and end of the actions. Compared with state-of-the-art approaches such as the Lagrangian Action Recognition Transformer (LART), YOLO-Act exhibits superior performance with a mean average precision (mAP) of 73.28 in experiments conducted on the AVA dataset, resulting in a gain of +28.18 mAP. Furthermore, YOLO-Act achieves this higher accuracy with significantly lower FLOPs, demonstrating its efficiency in computational resource utilization. The results highlight the advantages of incorporating precise tracking, effective spatial detection, and temporal consistency to address the challenges associated with video-based action detection.