Traditional video analytics are typically performed on a single node having limited
processing power and vertical scalability, resulting in a lack of real-time performance and …

Real-time deep video analytic at the edge is an enabling technology for emerging
applications, such as vulnerable road user detection for autonomous driving, which requires …

Detection-driven video analytics is resource hungry as it depends on running object
detectors on video frames. Running an object detection engine (ie, deep learning models …

We demonstrate criticality-aware canvas-based processing of multiple concurrent camera
streams at the resource constrained edge to show substantial improvement in the accuracy …

In the realm of autonomous vehicles, object detection holds a pivotal role in enabling
accurate perception and safe navigation within complex environments. This study …

Detection-driven real-time video analytics require continuous detection of objects contained
in the video frames using deep learning models like YOLOV3, EfficientDet, etc. However …

Adaptive and efficient computer vision systems have been proposed to make computer
vision tasks, eg, object classification and object detection, optimized for embedded boards …

Real-time intelligent video processing on embedded devices with low power consumption
can be useful for applications like drone surveillance, smart cars, and more. However, the …

In recent years, machine learning research has largely shifted focus from the cloud to the
edge. While the resulting algorithm-and hardware-level optimizations have enabled local …

Video Object Detection (VOD) is one of the fundamental problems in video understanding
with applications ranging from surveillance to autonomous driving. But many such real-world …