Dynamic swarm particle for fast motion vehicle tracking

Abstract

Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low‐cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.

Minat Mahasiswa Menggunakan Halodoc Selama Pandemi Covid-19

“…Minat merupakan tingkatan perilaku seseorang melakukan hal tertentu. Minat dalam penggunaan teknologi didefinisikan sebagai keinginan menggunakan sistem atau teknologi secara berkelanjutan (15). Minat tersebut merupakan bentuk penerimaan pengguna dalam hal: 1) Performance expectancy, tingkat kepercayaan pengguna dalam penggunaan teknologi membantunya meningkatkan kinerja atau memberikan keuntungan, 2) Effort expectancy, tingkat kemudahan menggunakan teknologi, 3) Social Influence, tingkat dimana seseorang harus menggunakan teknologi tersebut, dan 4) Facilitating conditions, merupakan komponen organisasi atau dukungan fasilitas untuk menggunakan sistem.…”

A Formal and Quantifiable Log Analysis Framework for Test Driving of Autonomous Vehicles

“…To identify the cause of the error, we carried out a process in the following order: (1) identify the experiment that had the problem, (2) check the detail log using the logging data defined above, (3) in the detailed log, confirm which part of the system had the problem, and (4) precise analysis is performed about the corresponding part.…”

“…The experiment was carried out by selecting a random distance from the original path. Each test case did not overlap each other to be suitable for MR 3 .…”

“…The autonomous vehicle (AV) industry is growing fast thanks to the improvement of artificial intelligence (AI) technologies based on sensing devices [1][2][3]. By now, many AVs from tens of companies are being driven in real road environments for safety and performance tests [4][5][6].…”

Road intersections vehicle tracking based on multi-layer graphs and multi-angle

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