The detection of objects via neural networks plays a key role in various video analytics, but
consumes huge resources. Due to the limited computing capability at edges, such real-time …

The detection of objects via neural networks plays a key role in various video analytics, but
consumes huge resources. Due to the limited on-device computing capability, such real-time …

To deal with the challenges in video object detection (VOD), such as occlusion and motion
blur, many state-of-the-art video object detectors adopt a feature aggregation module to …

Object detection is crucial in video analytics pipelines, but there is a need to optimize deep
neural networks (DNNs)-based object detection for resource-constrained Internet of Things …

Reliable object detection is essential for smart surveillance systems, and video surveillance
technology has been significantly improved thanks to Deep-Learning related techniques …

Detecting objects in a video is a compute-intensive task. In this paper we propose CaTDet, a
system to speedup object detection by leveraging the temporal correlation in video. CaTDet …

Real-time on-device object detection for video analytics fails to meet the accuracy
requirement due to limited resources of mobile devices while offloading object detection …

While single-image object detectors can be naively applied to videos in a frame-by-frame
fashion, the prediction is often temporally inconsistent. Moreover, the computation can be …

This paper proposes a method that jointly learns to detect objects at the current frame and
forecast the next frame's future feature map. Previous offline detectors have shown the …

The deep learning technique has recently led to significant improvement in object detection
accuracy. In many applications, object detection is performed on video data consisting of a …