Heterogeneous CPU-GPU tracking-learning-detection (H-TLD) for real-time object tracking

2019-04-01
Gurcan, Ilker
Temizel, Alptekin
The recently proposed tracking-learning-detection (TLD) method has become a popular visual tracking algorithm as it was shown to provide promising long-term tracking results. On the other hand, the high computational cost of the algorithm prevents it being used at higher resolutions and frame rates. In this paper, we describe the design and implementation of a heterogeneous CPU-GPU TLD (H-TLD) solution using OpenMP and CUDA. Leveraging the advantages of the heterogeneous architecture, serial parts are run asynchronously on the CPU while the most computationally costly parts are parallelized and run on the GPU. Design of the solution ensures keeping data transfers between CPU and GPU at a minimum and applying stream compaction and overlapping data transfer with computation whenever such transfers are necessary. The workload is balanced for a uniform work distribution across the GPU multiprocessors. Results show that 10.25 times speed-up is achieved at 1920 x 1080 resolution compared to the baseline TLD. The source code has been made publicly available to download from the following address: http://gpuresearch.ii.metu.edu.tr/codes/.
JOURNAL OF REAL-TIME IMAGE PROCESSING

Suggestions

Fully-Automatic Target Detection and Tracking for Real-Time, Airborne Imaging Applications
Alkanat, Tunc; Tunali, Emre; Oz, Sinan (2015-03-14)
In this study, an efficient, robust algorithm for automatic target detection and tracking is introduced. Procedure starts with a detection phase. Proposed method uses two alternatives for the detection phase, namely maximally stable extremal regions detector and Canny edge detector. After detection, regions of interest are evaluated and eliminated according to their compactness and effective saliency. The detection process is repeated for a predetermined number of pyramid levels where each level processes a...
Extended Object Tracking and Shape Classification
Tuncer, Barkın; Kumru, Murat; Alatan, Abdullah Aydın; Özkan, Emre (2018-07-10)
Recent extended target tracking algorithms provide reliable shape estimates while tracking objects. The estimated extent of the objects can also be used for online classification. In this work, we propose to use a Bayesian classifier to identify different objects based on their contour estimates during tracking. The proposed method uses the uncertainty information provided by the estimation covariance of the tracker.
Neural network method for direction of arrival estimation with uniform cylindrical microstrip patch array
Caylar, S.; Dural, G.; Leblebicioğlu, Mehmet Kemal (Institution of Engineering and Technology (IET), 2010-02-01)
In this study, a new neural network algorithm is proposed for real-time multiple source tracking problem with cylindrical patch antenna array based on a previously reported Modified Neural Multiple Source Tracking (MN-MUST) algorithm. The proposed algorithm, namely cylindrical microstrip patch array modified neural multiple source tracking (CMN-MUST) algorithm implements MN-MUST algorithm on a cylindrical microstrip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microst...
OCCLUSION-AWARE HMM-BASED TRACKING BY LEARNING
Marpuc, Tughan; Alatan, Abdullah Aydın (2014-10-30)
Recently, an emerging class of methods, namely tracking by detection, achieved quite promising results on challenging tracking data sets. These techniques train a classifier in an online manner to separate the object from its background. These methods only take input location of the object and a random feature pool; then, a classifier bootstraps itself by using the current tracker state and extracted positive and negative samples. Following these approaches, a novel tracking system is proposed. A feature se...
Multi-scan data association algorithm for multitarget tracking
Ağırnas, Emre; Demirbaş, Kerim; Department of Electrical and Electronics Engineering (2004)
Data association problem for multitarget tracking is determination of the relationship between targets and the incoming measurements from sensors of the target tracking system. Performance of a multitarget tracking system is strongly related to the chosen method for data association and target tracking algorithm. Incorrect data association effects state estimation of targets. In this thesis, we propose a new multi-scan data association algorithm for multitarget tracking systems. This algorithm was implement...
Citation Formats
I. Gurcan and A. Temizel, “Heterogeneous CPU-GPU tracking-learning-detection (H-TLD) for real-time object tracking,” JOURNAL OF REAL-TIME IMAGE PROCESSING, pp. 339–353, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30051.