Object tracking and recognition using massively parallel processing with CUDA

Zorski, Witold; Sklodowski, Piotr

doi:10.1109/mmar.2015.7284011

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…CUDA is a general purpose parallel computing platform and programming model that it leverages the parallel compute engine in NVIDIA GPUs to solve many complex computational problems in a more efficient way. Using a machine learning method to choose the best sparse matrix representation model on GPU [13], the target tracking algorithm was implemented on the CUDA platform with the GPU's ability of parallel computing [14], [15], etc. Pei [6] proposed an improved tracking method based on Struck [7], the extended affine transformation motion estimation method had been adopted to automatically adjust the tracking window's scale and rotation.…”

Section: Related Workmentioning

confidence: 99%

Speed Optimization of UAV Vehicle Tracking Algorithm

Cai

Pei

et al. 2017

IFIP Advances in Information and Communication Technology

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Section: Related Workmentioning

confidence: 99%

Speed Optimization of UAV Vehicle Tracking Algorithm

Cai

Pei

et al. 2017

IFIP Advances in Information and Communication Technology

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Speed optimization of UAV vehicle tracking algorithm

Xu¹,

Cai²,

Pei³

et al. 2017

2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS)

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An improved method for vehicle tracking in UAV-captured video is introduced in this paper, this method based on GPU (Graphics Processing Units) acceleration which have efficient parallel computing powers. Today, vehicle tracking in UAV videos has many meaningful applications in computer vision, such as traffic management and illegal vehicle tracking. However, the increased computational complexity makes it difficult to be used in real-time tracking system. To overcome the limitation of tracking speed and meet the highly intensive calculation required, we perform image processing with GPUs and adopt an optimized structured output support vector machine (SVM) to online learning. The proposed approach is evaluated in UAV videos taken from DJI Matrice 100 drone and the corresponding conclusions are received.

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Quaternion-based determination of 3D objects orientation

Zorski

2017

2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR)

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Object tracking and recognition using massively parallel processing with CUDA

Cited by 4 publications

References 18 publications

Speed Optimization of UAV Vehicle Tracking Algorithm

Speed Optimization of UAV Vehicle Tracking Algorithm

Speed optimization of UAV vehicle tracking algorithm

Quaternion-based determination of 3D objects orientation

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