A. Ayub Khan
Papers
1
Total Citations
3
H-Index
1
About
A. Ayub Khan is a researcher at the forefront of intelligent video surveillance and deep learning, with a primary focus on advancing object detection and tracking methodologies. His most notable contribution, "Missing Object Detection and Tracking from Video Surveillance Camera Footage Based on Deep Learning Through Convolutional Gated Recurrent Neural Networks (CGRNN)" (2025), introduces a novel hybrid architecture that fuses convolutional neural networks with gated recurrent units to enhance the accuracy and temporal coherence of object tracking in complex surveillance environments. This work, already garnering 3 citations in its early publication phase, addresses critical challenges in real-time anomaly detection and forensic video analysis. Khan’s research bridges the gap between spatial feature extraction and sequential pattern recognition, offering robust solutions for automated surveillance systems. His achievements underscore a commitment to applying cutting-edge AI to practical security challenges, making his work highly relevant for students and researchers exploring deep learning applications in computer vision, video analytics, and smart city infrastructure.
Research Focus
Key Achievements
Top Papers
- 1