A Distributed Tracking Algorithm for Counting People in Video

Kuplyakov, Denis; Shalnov, Evgeny; Konushin, Vadim; Konushin, Anton

doi:10.1134/s0361768819040042

Cited by 9 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Это позволяет значительно снизить объем и повысить качество данных, передаваемых в облако, повышая качество работы системы интеллектуального анализа данных [20,30,34].…”

Section: рис 7 модель туманных вычисленийunclassified

Digital Twin of a City: Concept Overview

Иванов¹,

Никольская²,

Радченко³

et al. 2020

CMSE

View full text Add to dashboard Cite

Section: рис 7 модель туманных вычисленийunclassified

Digital Twin of a City: Concept Overview

Иванов¹,

Никольская²,

Радченко³

et al. 2020

CMSE

View full text Add to dashboard Cite

“…Предложен алгоритм подсчета автомобилей, разработанный на основе [1]. 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.…”

Section: заключениеunclassified

“…За счет распределенности обработки и применения детекции не ко всем кадрам, а к их разряженному множеству, удается снизить требуемое число вычислительных ресурсов, увеличить производительность и получить алгоритм, способный качественно работать в реальном времени. Адаптируется и модифицируется подход, ранее предложенный для отслеживания людей [1]. Улучшение модуля оценки скорости и уточнение модели движения позволяют уменьшить частоту детекции в 3 раза.…”

unclassified

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

View full text Add to dashboard Cite

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.

show abstract

“…From the above mathematical depiction (14), ' ' represent a backdrop subtraction procedure in a third hidden layer at a time ' . After completing the backdrop subtraction process, significant features is extracted at fourth hidden layer.…”

Section: Figure 1 Architecture Diagram Of Assc-dnl Technique For Movimentioning

confidence: 99%

“…A robust person tracking method was presented in [13] with the help of particle swarm optimization (PSO) algorithm for multiple persons in real-time video. A distributed tracking algorithm was proposed in [14] to decrease the number of computational resources owing to detecting people in a sparse set of frames.…”

Section: Literature Surveymentioning

confidence: 99%

Anisotropic Sophisticated Spatio-Temporal Contours Based Deep Neural Learned Moving Objects Detection in Video

Kader*,

Nihal

2019

IJRTE

View full text Add to dashboard Cite

Object detection in the video sequence is a significant problem to be resolved in image processing because it used different applications in video compression, video surveillance, robot technology, etc. Few research works have been designed in conventional works to discover moving objects using various machine learning techniques. However, dynamic changing background, object size variations and degradation of video frames during the object detection process remained an open issue. In order to overcome such limitations, Anisotropic Sophisticated Spatiotemporal Contours based Deep Neural Network Learning (ASSC-DNNL) practice is projected. ASSC-DNL Technique initially obtains a number of video file as input at the input layer. After acquiring the video, input layer forward it to hidden layers. Subsequently, ASSC-DNL Technique accomplishes the encoding process in the first hidden layer using Anisotropic Stacked Autoencoder (ASA). During the encoding process, ASSC-DNL practice maps each video frames pixels in input video via code. This practice results in compressed video with enhanced quality. Afterward, ASSC-DNL practice transforms compressed video into a numeral of frames in the second concealed layer. Followed by, ASSC-DNL practice carried out Teknomo–Fernandez Spatiotemporal Based Background Subtraction (TS-BS) process at the third hidden layer, in which it effectively segments the foreground images from dynamic changing background. Then, ASSC-DNL practice deep analyzes the foreground image of video frames and mines some features like shape, color, texture, intensity, and size. Finally, ASSC-DNL Technique exactly finds the moving objects in video frames according to identified features with minimal time at the output layer. Therefore, ASSC-DNL Technique obtains enhanced moving objects detection performance when compared to existing works. The simulation of ASSC-DNL practice is conducted via different metrics such as accuracy, time and false positive rate towards in detection.

show abstract

A Distributed Tracking Algorithm for Counting People in Video

Cited by 9 publications

References 10 publications

Digital Twin of a City: Concept Overview

Digital Twin of a City: Concept Overview

Algorithm for Counting Cars in Large-scale Video Surveillance Systems

Anisotropic Sophisticated Spatio-Temporal Contours Based Deep Neural Learned Moving Objects Detection in Video

Contact Info

Product

Resources

About