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 …
A survey of recent advances in cnn-based single image crowd counting and density estimation
VA Sindagi, VM Patel - Pattern Recognition Letters, 2018 - Elsevier
Estimating count and density maps from crowd images has a wide range of applications
such as video surveillance, traffic monitoring, public safety and urban planning. In addition …
such as video surveillance, traffic monitoring, public safety and urban planning. In addition …
Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
The rampant coronavirus disease 2019 (COVID-19) has brought global crisis with its deadly
spread to more than 180 countries, and about 3,519,901 confirmed cases along with …
spread to more than 180 countries, and about 3,519,901 confirmed cases along with …
Features for multi-target multi-camera tracking and re-identification
Abstract Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video
taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images …
taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images …
Performance measures and a data set for multi-target, multi-camera tracking
To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a
new pair of precision-recall measures of performance that treats errors of all types uniformly …
new pair of precision-recall measures of performance that treats errors of all types uniformly …
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 …
Decidenet: Counting varying density crowds through attention guided detection and density estimation
In real-world crowd counting applications, the crowd densities vary greatly in spatial and
temporal domains. A detection based counting method will estimate crowds accurately in …
temporal domains. A detection based counting method will estimate crowds accurately in …
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 …
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 …
Deepid-net: Deformable deep convolutional neural networks for object detection
In this paper, we propose deformable deep convolutional neural networks for generic object
detection. This new deep learning object detection diagram has innovations in multiple …
detection. This new deep learning object detection diagram has innovations in multiple …