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| 2. |
- Kristanl, Matej, et al.
(författare)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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| 3. |
- Abdul Waheed, Malik, 1981-, et al.
(författare)
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Generalized Architecture for a Real-time Computation of an Image Component Features on a FPGA
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This paper describes a generalized architecture for real-time component labeling and computation of image component features. Computing real-time image component features is one of the most important paradigms for modern machine vision systems. Embedded machine vision systems demand robust performance, power efficiency as well as minimum area utilization. The presented architecture can easily be extended with additional modules for parallel computation of arbitrary image component features. Hardware modules for component labeling and feature calculation run in parallel. This modularization makes the architecture suitable for design automation. Our architecture is capable of processing 390 video frames per second of size 640x480 pixels. Dynamic power consumption is 24.20mW at 86 frames per second on a Xilinx Spartran6 FPGA.
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| 4. |
- Cheng, Xin, 1974-, et al.
(författare)
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Color Symbol Design and Its Classification for Optical Navigation
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We explored the color symbol design and its recognition in image as reference structure for optical navigation. A colors pair was first determined as foreground and background from HSI color palette and then a color symbol was designed as reference structure. The advantage of using this selected color symbol is a significant reduction, up to 97%, of segmented image components as compared to the grey scale image used. The reduction of segmented components in image will result in saving the hardware resources e.g. memory and processing power which are very important constraint for embedded platforms. A color symbol pattern was designed, comprising of three concentric circles with selected color pair. Inside the inner most circle is the Area Of Interest (AOI), the contents of AOI depends on the particular application. A hardware centric image analysis algorithm is developed for easy and robust recognition. Image components are identified after preprocessing, segmentation and labeling. The color symbol can be recognized at a classification step. Evaluating a variety of viewing angles and reading distances ranging from 30 to 150 degrees and from 1 to 10 meters gives a classification success rate of 72 percent of the positions.
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| 5. |
- Cheng, Xin, 1974-, et al.
(författare)
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Hardware Centric Machine Vision for High Precision Center of Gravity Calculation
- 2010
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Ingår i: PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY. ; 40, s. 576-583
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Konferensbidrag (refereegranskat)abstract
- We present a hardware oriented method for real-time measurements of object’s position in video. The targeted application area is light spots used as references for robotic navigation. Different algorithms for dynamic thresholding are explored in combination with component labeling and Center Of Gravity (COG) for highest possible precision versus Signal-to-Noise Ratio (SNR). This method was developed with a low hardware cost in focus having only one convolution operation required for preprocessing of data.
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| 6. |
- Cheng, Xin, 1974-, et al.
(författare)
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Optimized Color Pair Selection for Label Design
- 2011
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Ingår i: Proceedings Elmar - International Symposium Electronics in Marine. - Zadar, Croatia : IEEE conference proceedings. - 9789537044121 ; , s. 115-118
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Konferensbidrag (refereegranskat)abstract
- We present in this paper a technique for designing reference labels that can be used for optical navigation. We optimize the selection of foreground and background colors used for the printed reference labels. This optimization calibrates for individual color responses among printers and cameras such that the Signal to Noise Ratio (SNR) is maximized. Experiments show that we get slightly smaller SNR for the color labels compared to using a monochrome technique. However, the number of segmented image components is reduced significantly by as much as 78 percent. This reduction of number of image components will in turn reduce the memory storage requirement for the computing embedded system.
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| 7. |
- Malik, Abdul Waheed, 1981-, et al.
(författare)
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Real-time Component Labelling with Centre of Gravity Calculation on FPGA
- 2011
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Ingår i: 2011 Proceedings of Sixth International Conference on Systems.
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a hardware unit for real time component labelling with Centre of Gravity (COG) calculation. The main targeted application area is light spots used as references for robotic navigation. COG calculation can be done in parallel with a single pass component labelling unit without first having to resolve merged labels. We present hardware architecture suitable for implementation of this COG unit on Field programmable Gate Arrays (FPGA). As result, we get high frame speed, low power and low latency. The device utilization and estimated power dissipation are reported for Xilinx Virtex II pro device simulated at 86 VGA sized frames per second. Maximum speed is 410 frames per second at 126 MHz clock.
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