Estimation of the people position in the world coordinate system for video surveillance

Shalnov, Evgeny; Gringauz, Asher; Konushin, Anton

doi:10.1134/s0361768816060062

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…For the future work we plan to use automatic camera calibration algorithms (see fig. 5) [20,21]. This will allow to perform person tracking on the ground map and further improve accuracy of people counting.…”

Section: Resultsmentioning

confidence: 99%

A Distributed Tracking Algorithm for Counting People in Video by Head Detection

Kuplyakov

Geraskin

Mamedov

et al. 2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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We consider the problem of people counting in video surveillance. This is one of the most popular tasks in video analysis because this data can be used for predictive analytics and improvement of customer services, traffic control, etc. Our method is based on the object tracking in video with low framerate. We use the algorithm from [1] as a baseline and propose several modifications that improve the quality of people counting. One of the main modifications is to use a head detector instead of a body detector in the tracking pipeline. Head tracking is proved to be more robust and accurate as the heads are less susceptible to occlusions. To find the intersection of a person with a signal line, we either raise the signal lines to the level of the heads or perform a regression of bodies based on the available head detections. Our experimental evaluation has demonstrated that the modified algorithm surpasses the original in both ac- curacy and computational efficiency, showing a lower counting error on a lower detection frequency.

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Section: Resultsmentioning

confidence: 99%

A Distributed Tracking Algorithm for Counting People in Video by Head Detection

Kuplyakov

Geraskin

Mamedov

et al. 2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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“…Это различие является существенным, в первом случае мы можем судить только о пространственным расположении автомобиля, а во втором нам известно направление движения и часть траектории. Для более точного сопоставления обнаружений можно проводить вычисления не в плоскости кадра, а в координатах земли [13].…”

Section: обзор существующих методовunclassified

“…Частота детекций была снижена более чем в 3 раза по сравнению с базовым методом, что позволяет сократить объем вычислительных ресурсов, требуемых для детекции, и увеличить скорость работы без потери качества. В будущем планируется перейти от плоскости кадра к мировой системе координат за счёт автоматической калибровки камеры [13]. Это сделает модель движения фильтра Калмана более точной, что улучшит качество трекинга.…”

Section: заключениеunclassified

Algorithm for Counting Cars in Large-scale Video Surveillance Systems

Shirokov

Kuplyakov

Konushin

2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 1

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The article deals with the problem of counting cars in large-scale video surveillance systems. The proposed method is based on car tracking and counting the number of tracks intersecting the given signal line. We use a distributed tracking algorithm. It reduces the amount of necessary computational resources and increases performance up to realtime by detecting vehicles in a sparse set of frames. We adapted and modified the approach previously proposed for people tracking. Proposed improvement of the speed estimation module and refinement of the motion model reduced the detection frequency by 3 times. The experimental evaluation shows that the proposed algorithm allows reaching an acceptable counting quality with a detection frequency of 3 Hz.

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