People detection and counting is becoming of great relevance in many application fields which range from the video surveillance of shopping centres to the management of public services such as bus stops. Various computer vision applications exploit Deep Learning technology for people recognition which recently showed great achievements. A very important task along with object/people detection is their tracking which is of fundamental importance especially when counting has to be performed. In this research work, a specific application for people counting at a bus stop is proposed, taking advantage of NVIDIA DeepStream SDK 5.0 detection and tracking algorithms. The final number of people is obtained through a Kalman Filter which aims at avoiding occlusion issues which are typical of people detection-tracking applications. The experimental results show the effectiveness of this approach in three scenarios with different complexity, significantly reducing the counting error with respect to pure detection counting.

Improved Person Counting Performance Using Kalman Filter Based on Image Detection and Tracking

Vignarca, Daniele;Prakash, Jai;Vignati, Michele;Sabbioni, Edoardo
2021

Abstract

People detection and counting is becoming of great relevance in many application fields which range from the video surveillance of shopping centres to the management of public services such as bus stops. Various computer vision applications exploit Deep Learning technology for people recognition which recently showed great achievements. A very important task along with object/people detection is their tracking which is of fundamental importance especially when counting has to be performed. In this research work, a specific application for people counting at a bus stop is proposed, taking advantage of NVIDIA DeepStream SDK 5.0 detection and tracking algorithms. The final number of people is obtained through a Kalman Filter which aims at avoiding occlusion issues which are typical of people detection-tracking applications. The experimental results show the effectiveness of this approach in three scenarios with different complexity, significantly reducing the counting error with respect to pure detection counting.
2021 AEIT International Conference on Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)
978-88-87237-52-8
File in questo prodotto:
File Dimensione Formato  
C2021_AEIT_Improved_Person_Counting_Performance_Using_Kalman_Filter_Based_on_Image_Detection_and_Tracking.pdf

Accesso riservato

Descrizione: Articolo principale
: Publisher’s version
Dimensione 3.26 MB
Formato Adobe PDF
3.26 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1195435
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact