Deep Learning Based Object Recognition via Video Analysis
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 19
Special Issue Editor
Special Issue Information
Dear Colleagues,
Driven by the explosive growth of camera‐based sensing platforms—ranging from UAVs and autonomous vehicles to wearable and smart-city surveillance systems—there is an urgent demand for algorithms that can reliably recognize and track objects in continuous video streams. Traditional vision pipelines built on handcrafted features and sequential motion models have laid important groundwork; however, their fusion with deep neural networks has unleashed a new generation of spatio-temporal architectures capable of real-time object detection, multi-object tracking, re-identification, and fine-grained scene understanding. State-of-the-art transformers and diffusion‐based networks, self-supervised pre-training on large-scale video corpora, and efficient model compression now deliver unprecedented accuracy while still meeting the tight latency and power constraints imposed by embedded sensor nodes.
Despite this rapid progress, the field continues to grapple with challenges in domain generalization across diverse camera geometries, resilience to occlusion and adverse illumination, data-efficient training, privacy protection, and explainability—all of which remain fertile ground for innovation. Research efforts are, therefore, pushing toward lightweight, interpretable, and ethically responsible solutions that integrate multi-modal cues (e.g., LiDAR, radar, and audio) and support downstream tasks such as behavior prediction and situational awareness.
The focus of this Special Issue sits squarely within the mandate of MDPI Sensors. Video cameras are among the most ubiquitous and information-rich sensing modalities, and turning their raw pixel streams into actionable, object-level intelligence is central to many of the journal’s core application areas, including autonomous navigation, industrial automation, environmental monitoring, smart healthcare, and security. By highlighting novel algorithms, datasets, and hardware–software co-designs that advance object recognition in video, this Special Issue aims to catalyze cross-disciplinary research that converts sensor data into trustworthy insight—precisely the mission of the Sensors journal.
Dr. Yawen Lu
Guest Editor
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Keywords
- video-based object detection
- multi-object tracking and re-identification
- self-supervised video representation learning
- sensor fusion (RGB-LiDAR-radar)
- domain adaptation and generalization in video analytics
- adversarial robustness and privacy in video sensing
- explainable AI for object recognition
- autonomous navigation and smart-city surveillance
- real-time video scene understanding
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