scholarly journals MODIFIED SURF ALGORITHM IMPLEMENTATION ON FPGA FOR REAL-TIME OBJECT TRACKING / MODIFIKUOTO POŽYMIŲ VAIZDE IŠSKYRIMO SURF ALGORITMO OBJEKTUI SEKTI REALIUOJU LAIKU ĮGYVENDINIMAS LAUKU PROGRAMUOJAMOJE LOGINĖJE MATRICOJE

2013 ◽  
Vol 5 (2) ◽  
pp. 74-78
Author(s):  
Tomyslav Sledevič
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Video Stream ◽  
Speeded Up Robust Features ◽  
Point Of Interest ◽  
Process Implementation ◽  
Orientation Vector ◽  
Hessian Determinant ◽  
Algorithm Implementation ◽  
Local Extreme

The paper describes the FPGA-based implementation of the modified speeded-up robust features (SURF) algorithm. FPGA was selected for parallel process implementation using VHDL to ensure features extraction in real-time. A sliding 84×84 size window was used to store integral pixels and accelerate Hessian determinant calculation, orientation assignment and descriptor estimation. The local extreme searching was used to find point of interest in 8 scales. The simplified descriptor and orientation vector were calculated in parallel in 6 scales. The algorithm was investigated by tracking marker and drawing a plane or cube. All parts of algorithm worked on 25 MHz clock. The video stream was generated using 60 fps and 640×480 pixel camera. Article in Lithuanian Santrauka Pateikiamas modifikuoto požymių vaizde išskyrimo algoritmo SURF įgyvendinimas lauku programuojamų loginių matricų (LPLM) įrenginiuose. LPLM įrenginiai pasirinkti dėl galimybės tuo pat metu įgyvendinti veikiančius procesus taikant VHDL kalbą. Tai garantuoja, kad požymiai vaizde bus išskirti realiuoju laiku. Skaičiavimams paspartinti taikomas slankusis 84×84 taškų dydžio langas, kuriame saugomas sudėtingas vaizdas. Šio slankiojo lango duomenys taikomi Hessian determinantui, būdingųjų taškų orientacijai ir deskriptoriams apskaičiuoti. Požymiai ieškomi aštuoniose skalėse taikant lokalių ekstremumų paiešką. Požymių orientacijos vektorius ir supaprastintas deskriptorius skaičiuojami šešiose skalėse tuo pat metu. Algoritmo veikimas tiriamas sekant keturių taškų žymeklį ir pagal jį braižant plokštumą arba erdvinį kubą. Skaičiuojama 25 MHz taktiniu dažniu. Vaizdui gauti taikoma 60 kadrų per sekundę dažnio 640×480 taškų raiškos vaizdo kamera.

scholarly journals Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking

2020 ◽  
Author(s):  
Dominika Przewlocka ◽  
Mateusz Wasala ◽  
Hubert Szolc ◽  
Krzysztof Blachut ◽  
Tomasz Kryjak
Keyword(s):  
Neural Network ◽  
Object Tracking ◽  
Real Time ◽  
Video Stream ◽  
Visual Object ◽  
Vision Systems ◽  
Embedded Vision ◽  
Network Training ◽  
Binary Networks ◽  
Embedded Vision Systems

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations-from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover , quantisation of weights positively affects the network training by decreasing overfitting.

scholarly journals Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking

2020 ◽  
Author(s):  
Dominika Przewlocka ◽  
Mateusz Wasala ◽  
Hubert Szolc ◽  
Krzysztof Blachut ◽  
Tomasz Kryjak
Keyword(s):  
Neural Network ◽  
Object Tracking ◽  
Real Time ◽  
Video Stream ◽  
Visual Object ◽  
Vision Systems ◽  
Embedded Vision ◽  
Network Training ◽  
Binary Networks ◽  
Embedded Vision Systems

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations-from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover , quantisation of weights positively affects the network training by decreasing overfitting.

Analysis of visual object tracking algorithms for real-time systems

2021 ◽  
pp. 59-65
Author(s):  
Mykola Moroz ◽  
Denys Berestov ◽  
Oleg Kurchenko
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Video Stream ◽  
Target Object ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Security Systems ◽  
Visual Support ◽  
Tracking Object ◽  
Time Systems

The article analyzes the latest achievements and decisions in the process of visual support of the target object in the field of computer vision, considers approaches to the choice of algorithm for visual support of objects on video sequences, highlights the main visual features that can be based on tracking object. The criteria that influence the choice of the target object-tracking algorithm in real time are defined. However, for real-time tracking with limited computing resources, the choice of the appropriate algorithm is crucial. The choice of visual tracking algorithm is also influenced by the requirements and limitations for the monitored objects and prior knowledge or assumptions about them. As a result of the analysis, the Staple tracking algorithm was preferred, according to the criterion of speed, which is a crucial indicator in the design and development of software and hardware for automated visual support of the object in real-time video stream for various surveillance and security systems, monitoring traffic, activity recognition and other embedded systems.

Real-time multiple object tracking using deep learning methods

2021 ◽  
Author(s):  
Dimitrios Meimetis ◽  
Ioannis Daramouskas ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Real Time ◽  
Multiple Object Tracking ◽  
Learning Methods ◽  
Multiple Object
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