FPGA-Based Pedestrian Detection for Collision Prediction System

Cambuim, Lucas F. S.; Barros, Edna

doi:10.3390/s22124421

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Other studies have explored the use of Field-Programmable Gate Arrays (FPGAs) for hardware acceleration [9,10]. However, programming them can be complex as it requires knowledge and expertise in hardware programming and optimization.…”

Section: Related Workmentioning

confidence: 99%

MCFP-YOLO Animal Species Detector for Embedded Systems

Ibraheam,

Li,

Gebali

2023

Electronics

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Advances in deep learning have led to the development of various animal species detection models suited for different environments. Building on this, our research introduces a detection model that efficiently handles both batch and real-time processing. It achieves this by integrating a motion-based frame selection algorithm and a two-stage pipelining–dataflow hybrid parallel processing approach. These modifications significantly reduced the processing delay and power consumption of the proposed MCFP-YOLO detector, particularly on embedded systems with limited resources, without trading off the accuracy of our animal species detection system. For field applications, the proposed MCFP-YOLO model was deployed and tested on two embedded devices: the RP4B and the Jetson Nano. While the Jetson Nano provided faster processing, the RP4B was selected due to its lower power consumption and a balanced cost–performance ratio, making it particularly suitable for extended use in remote areas.

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Section: Related Workmentioning

confidence: 99%

MCFP-YOLO Animal Species Detector for Embedded Systems

Ibraheam,

Li,

Gebali

2023

Electronics

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“…In general, problem on detecting people is handled using learning algorithms such as Support Vector Machines (SVM) [28], [29]. Then, the detected target is then tracked by Interactive Multiple Model (IMM) which has a constant velocity and constant turn rate models [30], [31].…”

mentioning

confidence: 99%

Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker

Zainudin

Kodagoda

2023

IJRCS

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Consistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95\% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95\% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only.

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