Moving Object Detection in Heterogeneous Conditions in Embedded Systems
| Autor: | Stefano Quer, Alessandro Garbo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Pedestrian detection Real-time computing 02 engineering and technology lcsh:Chemical technology Biochemistry human detection Article Analytical Chemistry motion estimation Robustness (computer science) Atomic and Molecular Physics Motion estimation 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Software system Electrical and Electronic Engineering Instrumentation business.industry 020207 software engineering tracking Atomic and Molecular Physics and Optics Object detection automatic surveillance Embedded system Video tracking Automatic surveillance Embedded systems Human detection Tracking 020201 artificial intelligence & image processing embedded systems and Optics business |
| Zdroj: | Sensors; Volume 17; Issue 7; Pages: 1546 Sensors, Vol 17, Iss 7, p 1546 (2017) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s17071546 |
| Popis: | This paper presents a system for moving object exposure, focusing on pedestrian detection, in external, unfriendly, and heterogeneous environments. The system manipulates and accurately merges information coming from subsequent video frames, making small computational efforts in each single frame. Its main characterizing feature is to combine several well-known movement detection and tracking techniques, and to orchestrate them in a smart way to obtain good results in diversified scenarios. It uses dynamically adjusted thresholds to characterize different regions of interest, and it also adopts techniques to efficiently track movements, and detect and correct false positives. Accuracy and reliability mainly depend on the overall receipt, i.e., on how the software system is designed and implemented, on how the different algorithmic phases communicate information and collaborate with each other, and on how concurrency is organized. The application is specifically designed to work with inexpensive hardware devices, such as off-the-shelf video cameras and small embedded computational units, eventually forming an intelligent urban grid. As a matter of fact, the major contribution of the paper is the presentation of a tool for real-time applications in embedded devices with finite computational (time and memory) resources. We run experimental results on several video sequences (both home-made and publicly available), showing the robustness and accuracy of the overall detection strategy. Comparisons with state-of-the-art strategies show that our application has similar tracking accuracy but much higher frame-per-second rates. |
| Databáze: | OpenAIRE |
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