With the growth of the Internet of Things, we see an increase in the importance of analysis of
data from the edge, often with the results needed in real-time. Indy Car series is one of the …

Several recent works seek to create lightweight deep networks for video object detection on
mobiles. We observe that many existing detectors, previously deemed computationally …

Video-based vehicle detection has received considerable attention over recent decades.
Deep learning based algorithms are effective means for vehicle detection. However, these …

Successive frames of a video are highly redundant, and the most popular object detection
methods do not take advantage of this fact. Using multiple consecutive frames can improve …

Modern technologies are being developed to address the alarming rise in road accidents
caused by drivers' errors. We leverage computer vision and deep learning at the edge (ie, in …

This paper introduces an online model for object detection in videos with real-time
performance on mobile and embedded devices. Our approach combines fast single-image …

The existing methods for video object detection mainly depend on two-stage image object
detectors. The fact that two-stage detectors are generally slow makes it difficult to apply in …

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 …

This paper centers on analyzing the effect of automotive Ethernet camera image quality on
the object detection models. We choose the five pre-trained models from the TensorFlow …

It has been recognized that deeper and wider neural networks are continuously advancing
the state-of-the-art performance of various computer vision and machine learning tasks …