Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
There is increasing interest in video object detection for many situations, such as industrial processes, surveillance systems, and nature exploration. In this work, we were concerned with the detection of pedestrians in video sequences. The aim was to deal with issues associated with the background...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/11/5371 |
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| author | Juan Alberto Antonio Velázquez Marcelo Romero Huertas Roberto Alejo Eleuterio Everardo Efrén Granda Gutiérrez Federico Del Razo López Eréndira Rendón Lara |
| author_facet | Juan Alberto Antonio Velázquez Marcelo Romero Huertas Roberto Alejo Eleuterio Everardo Efrén Granda Gutiérrez Federico Del Razo López Eréndira Rendón Lara |
| author_sort | Juan Alberto Antonio Velázquez |
| collection | DOAJ |
| description | There is increasing interest in video object detection for many situations, such as industrial processes, surveillance systems, and nature exploration. In this work, we were concerned with the detection of pedestrians in video sequences. The aim was to deal with issues associated with the background, scale, contrast, or resolution of the video frames, which cause inaccurate detection of pedestrians. The proposed method was based on the combination of two techniques: motion detection by background subtraction (MDBS) and active shape models (ASM). The MDBS technique aids in the identification of a moving region of interest in the video sequence, which potentially includes a pedestrian; then, the ASM algorithm actively finds and adjusts the silhouette of the pedestrian. We tested the proposed MDBS + ASM method with video sequences from open repositories, and the results were favorable in scenes where pedestrians were in a well-illuminated environment. The mean fit error was up to 4.5 pixels. In contrast, in scenes where reflections, occlusions, or pronounced movement are present, the identification was slightly affected; the mean fit error was 8.3 pixels in the worst case. The main contribution of this work was exploring the potential of the combination of MDBS and ASM for performance improvements in the contour-based detection of a moving pedestrian walking in a controlled environment. We present a straightforward method based on classical algorithms which have been proven effective for pedestrian detection. In addition, since we were looking for a practical process that could work in real-time applications (for example, closed-circuit television video or surveillance systems), we established our approach with simple techniques. |
| first_indexed | 2024-03-10T01:31:59Z |
| format | Article |
| id | doaj.art-cd156070e7cc4fd9afeb33a02c5b226f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:31:59Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cd156070e7cc4fd9afeb33a02c5b226f2023-11-23T13:40:34ZengMDPI AGApplied Sciences2076-34172022-05-011211537110.3390/app12115371Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape ModelsJuan Alberto Antonio Velázquez0Marcelo Romero Huertas1Roberto Alejo Eleuterio2Everardo Efrén Granda Gutiérrez3Federico Del Razo López4Eréndira Rendón Lara5Technological Institute of Higher Studies of Jocotitlan, Jocotitlan 50700, MexicoFaculty of Engineering, Autonomous University of the State of Mexico, Toluca 50110, MexicoDivision of Postgraduate Studies and Research, National Technological of Mexico, Campus Toluca, Metepec 52149, MexicoUAEM University Center at Atlacomulco, Autonomous University of the State of Mexico, Atlacomulco 50450, MexicoDivision of Postgraduate Studies and Research, National Technological of Mexico, Campus Toluca, Metepec 52149, MexicoDivision of Postgraduate Studies and Research, National Technological of Mexico, Campus Toluca, Metepec 52149, MexicoThere is increasing interest in video object detection for many situations, such as industrial processes, surveillance systems, and nature exploration. In this work, we were concerned with the detection of pedestrians in video sequences. The aim was to deal with issues associated with the background, scale, contrast, or resolution of the video frames, which cause inaccurate detection of pedestrians. The proposed method was based on the combination of two techniques: motion detection by background subtraction (MDBS) and active shape models (ASM). The MDBS technique aids in the identification of a moving region of interest in the video sequence, which potentially includes a pedestrian; then, the ASM algorithm actively finds and adjusts the silhouette of the pedestrian. We tested the proposed MDBS + ASM method with video sequences from open repositories, and the results were favorable in scenes where pedestrians were in a well-illuminated environment. The mean fit error was up to 4.5 pixels. In contrast, in scenes where reflections, occlusions, or pronounced movement are present, the identification was slightly affected; the mean fit error was 8.3 pixels in the worst case. The main contribution of this work was exploring the potential of the combination of MDBS and ASM for performance improvements in the contour-based detection of a moving pedestrian walking in a controlled environment. We present a straightforward method based on classical algorithms which have been proven effective for pedestrian detection. In addition, since we were looking for a practical process that could work in real-time applications (for example, closed-circuit television video or surveillance systems), we established our approach with simple techniques.https://www.mdpi.com/2076-3417/12/11/5371pedestrian identificationmotion detectionbackground subtractionactive shape model |
| spellingShingle | Juan Alberto Antonio Velázquez Marcelo Romero Huertas Roberto Alejo Eleuterio Everardo Efrén Granda Gutiérrez Federico Del Razo López Eréndira Rendón Lara Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models Applied Sciences pedestrian identification motion detection background subtraction active shape model |
| title | Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models |
| title_full | Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models |
| title_fullStr | Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models |
| title_full_unstemmed | Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models |
| title_short | Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models |
| title_sort | pedestrian localization in a video sequence using motion detection and active shape models |
| topic | pedestrian identification motion detection background subtraction active shape model |
| url | https://www.mdpi.com/2076-3417/12/11/5371 |
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