Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter
Pedestrian flow statistics and analysis in public places is an important means to ensure urban safety. However, in recent years, a video-based pedestrian flow statistics algorithm mainly relies on binocular vision or a vertical downward camera, which has serious limitations on the application scene...
Main Authors: | Miao He, Haibo Luo, Bin Hui, Zheng Chang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-04-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/9/8/1624 |
Similar Items
-
Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model
by: Miao He, et al.
Published: (2019-01-01) -
Single Shot Multibox Detector With Kalman Filter for Online Pedestrian Detection in Video
by: Fan Yang, et al.
Published: (2019-01-01) -
Optimized object tracking technique using Kalman filter
by: Liana Ellen Taylor, et al.
Published: (2016-07-01) -
Robust Visual Tracking with Reliable Object Information and Kalman Filter
by: Hang Chen, et al.
Published: (2021-01-01) -
Kalman filtering : With a radar tracking implementation
by: Svanström, Fredrik
Published: (2013)