FPGA Implementation of Particle Filter Based Object Tracking in Video

Agrawal, Subhash Chand; Velmurugan, Rajbabu; Patkar, Sachin B.

doi:10.1109/ised.2012.41

Cited by 9 publications

(14 citation statements)

References 4 publications

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“…Synthesis results reveal that the implemented object tracking system utilizes approximately 53% FPGA slices and 35% Block RAMs (on-chip memory) on Xilinx ML510 (Virtex-5 FX130T) FPGA development platform. We have compared our proposed and implemented object tracking architecture with existing color histogram and/or particle filtering based object tracking implementations [35]- [37]. The comparison is shown in Table 2.…”

Section: Synthesis Resultsmentioning

confidence: 99%

“…But most of these algorithms discussed above for object tracking in dynamic backgrounds are computationally too expensive to be deployed on a moderate computing machine for a practical real-time tracking surveillance application. Recently, some researchers have combined color histogram with particle filtering framework to design computationally less expensive object trackers for dynamic background situations and achieved real-time performance by designing their hardware architectures [35]- [37]. In their implementations the maximum size considered for object of interest was 20x20 pixels [35] and the maximum number of particles considered were 31 [35].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some researchers have combined color histogram with particle filtering framework to design computationally less expensive object trackers for dynamic background situations and achieved real-time performance by designing their hardware architectures [35]- [37]. In their implementations the maximum size considered for object of interest was 20x20 pixels [35] and the maximum number of particles considered were 31 [35]. As the number of particles considered directly effects the robustness of the tracker, their robustness is relatively low.…”

Section: Introductionmentioning

confidence: 99%

“…better robustness, we have used 121 particles as compared to 31 particles by [35] and the maximum tracked object size of 100x100 pixels as compared to 20x20 pixels by [35]. For algorithmic verification, the simplified particle filtering and color histogram based object tracking scheme has been initially implemented in C/C++ programming language on a Dell Precision T3400 workstation (with Windows XP operating system, quadcore Intel® Core™2 Duo Processor with 2.93 GHz Operating Frequency, and 4GB RAM).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-time Object Tracking with Active PTZ Camera using Hardware Acceleration Approach

Singh¹,

Saini²,

Saurav³

et al. 2017

IJIGSP

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Abstract-This paper presents the design and implementation of a dedicated hardware (VLSI) architecture for real-time object tracking. In order to realize the complete system, the designed VLSI architecture has been integrated with different input/output video interfaces. These video interfaces along with the designed object tracking VLSI architecture have been coded using VHDL, simulated using ModelSim, and synthesized using Xilinx ISE tool chain. A working prototype of complete object tracking system has been implemented on Xilinx ML510 (Virtex-5 FX130T) FPGA board. The implemented system is capable of tracking the moving target object in real-time in PAL (720x576) resolution live video stream directly coming from the camera. Additionally, the implemented system also provides the real-time desired camera movement to follow the tracked object over a larger area.

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Section: Synthesis Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time Object Tracking with Active PTZ Camera using Hardware Acceleration Approach

Singh¹,

Saini²,

Saurav³

et al. 2017

IJIGSP

View full text Add to dashboard Cite

show abstract

“…An object tracking scheme based on particle filtering, color histogram and the sum of absolute differences that can handle camera movement and background change issues has been implemented. To increase the robustness of the system, we have considered 121 particles as compared to 31 particles by [62] and the maximum tracked object size of 100 × 100 pixels as compared to 32 × 32 pixels by [61]. To achieve real-time performance for PAL (720 × 576) resolution videos, for the implemented object tracking scheme, a dedicated VLSI architecture has been designed and developed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time FPGA-Based Object Tracker with Automatic Pan-Tilt Features for Smart Video Surveillance Systems

2017

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Abstract:The design of smart video surveillance systems is an active research field among the computer vision community because of their ability to perform automatic scene analysis by selecting and tracking the objects of interest. In this paper, we present the design and implementation of an FPGA-based standalone working prototype system for real-time tracking of an object of interest in live video streams for such systems. In addition to real-time tracking of the object of interest, the implemented system is also capable of providing purposive automatic camera movement (pan-tilt) in the direction determined by movement of the tracked object. The complete system, including camera interface, DDR2 external memory interface controller, designed object tracking VLSI architecture, camera movement controller and display interface, has been implemented on the Xilinx ML510 (Virtex-5 FX130T) FPGA Board. Our proposed, designed and implemented system robustly tracks the target object present in the scene in real time for standard PAL (720 × 576) resolution color video and automatically controls camera movement in the direction determined by the movement of the tracked object.

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FPGA Implementation of Particle Filters for Robotic Source Localization

2021

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Particle filtering is very reliable in modelling non-Gaussian and non-linear elements of physical systems, which makes it ideal for tracking and localization applications. However, a major drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. The re-sampling step in the particle filters creates a computational bottleneck since it is inherently sequential and cannot be parallelized. This paper proposes a modification to the existing particle filter algorithm, which enables parallel re-sampling and reduces the effect of the re-sampling bottleneck. We then present a high-speed and dedicated hardware architecture incorporating pipe-lining and parallelization design strategies to supplement the modified algorithm and lower the execution time considerably. From an application standpoint, we propose a novel source localization model to estimate the position of a source in a noisy environment using the particle filter algorithm implemented on hardware. The design has been prototyped using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 µs for processing 1024 particles (compared to 64 ms on Intel Core i7-7700 CPU with eight cores clocking at 3.60 GHz) and can be deployed for real-time applications.

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FPGA Implementation of Particle Filter Based Object Tracking in Video

Cited by 9 publications

References 4 publications

Real-time Object Tracking with Active PTZ Camera using Hardware Acceleration Approach

Real-time Object Tracking with Active PTZ Camera using Hardware Acceleration Approach

Real-Time FPGA-Based Object Tracker with Automatic Pan-Tilt Features for Smart Video Surveillance Systems

FPGA Implementation of Particle Filters for Robotic Source Localization

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