Pedestrian detection and counting method based on YOLOv5+DeepSORT

2021 ◽  
Author(s):  
qiu xiaofeng ◽  
sun xiangrui ◽  
chen yongchang ◽  
Wang Xinyan
Keyword(s):  
Pedestrian Detection ◽  
Counting Method ◽  
Detection And Counting

scholarly journals A Soft-YoloV4 for High-Performance Head Detection and Counting

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3096
Author(s):  
Zhen Zhang ◽  
Shihao Xia ◽  
Yuxing Cai ◽  
Cuimei Yang ◽  
Shaoning Zeng
Keyword(s):  
High Performance ◽  
Detection Rate ◽  
Detection Method ◽  
State Of The Art ◽  
Counting Method ◽  
People Counting ◽  
Detection Model ◽  
Detection And Counting ◽  
Head Detection ◽  
Ap Value

Blockage of pedestrians will cause inaccurate people counting, and people’s heads are easily blocked by each other in crowded occasions. To reduce missed detections as much as possible and improve the capability of the detection model, this paper proposes a new people counting method, named Soft-YoloV4, by attenuating the score of adjacent detection frames to prevent the occurrence of missed detection. The proposed Soft-YoloV4 improves the accuracy of people counting and reduces the incorrect elimination of the detection frames when heads are blocked by each other. Compared with the state-of-the-art YoloV4, the AP value of the proposed head detection method is increased from 88.52 to 90.54%. The Soft-YoloV4 model has much higher robustness and a lower missed detection rate for head detection, and therefore it dramatically improves the accuracy of people counting.

scholarly journals Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10132
Author(s):  
Robert Szczepanek
Keyword(s):  
Machine Learning ◽  
Public Space ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Computation Time ◽  
Movement Control ◽  
Time Lapse ◽  
Efficient Detection ◽  
Detection And Counting ◽  
Standard Application

At the turn of February and March 2020, COVID-19 pandemic reached Europe. Many countries, including Poland imposed lockdown as a method of securing social distance between potentially infected. Stay-at-home orders and movement control within public space not only affected the touristm industry, but also the everyday life of the inhabitants. The hourly time-lapse from four HD webcams in Cracow (Poland) are used in this study to estimate how pedestrian activity changed during COVID-19 lockdown. The collected data covers the period from 9 June 2016 to 19 April 2020 and comes from various urban zones. One zone is tourist, one is residential and two are mixed. In the first stage of the analysis, a state-of-the-art machine learning algorithm (YOLOv3) is used to detect people. Additionally, a non-standard application of the YOLO method is proposed, oriented to the images from HD webcams. This approach (YOLOtiled) is less prone to pedestrian detection errors with the only drawback being the longer computation time. Splitting the HD image into smaller tiles increases the number of detected pedestrians by over 50%. In the second stage, the analysis of pedestrian activity before and during the COVID-19 lockdown is conducted for hourly, daily and weekly averages. Depending on the type of urban zone, the number of pedestrians decreased from 33% in residential zones to 85% in tourist zones located in the Old Town. The presented method allows for more efficient detection and counting of pedestrians from HD time-lapse webcam images compared to SSD, YOLOv3 and Faster R-CNN. The result of the research is a published database with the detected number of pedestrians from the four-year observation period for four locations in Cracow.

Automatic Pedestrian Detection and Counting Applied to Urban Planning

2010 ◽  
pp. 285-289 ◽  
Author(s):  
Thomas Michelat ◽  
Nicolas Hueber ◽  
Pierre Raymond ◽  
Alexander Pichler ◽  
Pascal Schaal ◽  
...  
Keyword(s):  
Urban Planning ◽  
Pedestrian Detection ◽  
Detection And Counting
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