Deep reinforcement learning in computer vision: a comprehensive survey
Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …
the powerful representation of deep neural networks. Recent works have demonstrated the …
Computing systems for autonomous driving: State of the art and challenges
The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration) and the broad deployment of communication …
learning, and hardware acceleration) and the broad deployment of communication …
Motchallenge: A benchmark for single-camera multiple target tracking
Standardized benchmarks have been crucial in pushing the performance of computer vision
algorithms, especially since the advent of deep learning. Although leaderboards should not …
algorithms, especially since the advent of deep learning. Although leaderboards should not …
3d multi-object tracking: A baseline and new evaluation metrics
3D multi-object tracking (MOT) is an essential component for many applications such as
autonomous driving and assistive robotics. Recent work on 3D MOT focuses on develo** …
autonomous driving and assistive robotics. Recent work on 3D MOT focuses on develo** …
Computer vision for autonomous vehicles: Problems, datasets and state of the art
Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
Deep affinity network for multiple object tracking
Multiple Object Tracking (MOT) plays an important role in solving many fundamental
problems in video analysis and computer vision. Most MOT methods employ two steps …
problems in video analysis and computer vision. Most MOT methods employ two steps …
Spatial-temporal relation networks for multi-object tracking
Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is
a key to the success of trackers. A good similarity score is expected to reflect multiple cues …
a key to the success of trackers. A good similarity score is expected to reflect multiple cues …
Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism
In this paper, we propose a CNN-based framework for online MOT. This framework utilizes
the merits of single object trackers in adapting appearance models and searching for target …
the merits of single object trackers in adapting appearance models and searching for target …
Online multi-target tracking using recurrent neural networks
We present a novel approach to online multi-target tracking based on recurrent neural
networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges …
networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges …
Multiple object tracking: A literature review
Abstract Multiple Object Tracking (MOT) has gained increasing attention due to its academic
and commercial potential. Although different approaches have been proposed to tackle this …
and commercial potential. Although different approaches have been proposed to tackle this …