Human Tracking using Particle Filter
Jharna Majumdar,Kiran S +1 more
TL;DR: This paper proposes particle filter based methods for human tracking, addressing two major issues such as variations of distance measurement (similarity measure) and Re-Sampling algorithms.
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Abstract: Human tracking is the process of locating moving objects (human) over time using camera. It has wide number of applications like security and surveillance, traffic control, video editing, medical imaging etc. It can be a time consuming process due to the large amount of data contained in video. The objective of human tracking is to associate target objects in consecutive video frames. To initiate human tracking an algorithm analyzes video frames and outputs the movement of targets between the frames. There are a number of algorithms each having its own strengths and weakness. Considering the intended use is important when choosing the algorithm. This paper proposes particle filter based methods for human tracking, addressing two major issues such as variations of distance measurement (similarity measure) and Re-Sampling algorithms.
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Citations
CNN and HOG based comparison study for complete occlusion handling in human tracking
TL;DR: CNN is compared with HOG-SVM, which is described as the most successful human detection method and a hybrid Kalman-Particle Filter has been proposed, which outperformed PF and became much more prominent in the case of complete occlusion.
57
Comparative analysis of Machine Learning algorithms for Intrusion Detection
Vasudeva Pai,Devidas,N. D. Adesh +2 more
- 01 Jan 2021
TL;DR: The preliminary comparative study regarding which type of machine learning algorithm performs better in identifying the attacks namely Denial of Service, Probe, User to Root and Remote to Local is conducted.
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Implementation of real time particle filter based tracking algorithms on beagleboard-xm
Jharna Majumdar,Kiran S,Kiran Kumar K +2 more
- 01 Jan 2014
TL;DR: The development and realization of intelligent computer vision based target tracking algorithms on BeagleBoard-xM ARM based embedded platform and their accuracy analysis has been presented.
1
Real-time performance analysis of retinex algorithm on embedded boards for robotics application
Jharna Majumdar,Adarsh C,Harshpreet Singh,Rahul V C +3 more
- 15 Jun 2019
TL;DR: The work done on accelerating performance of the Retinex algorithms, SSR and MSR, using multithreading software optimization and hardware optimization, on the embedded platforms- UDOO x86 Ultra and Nvidia Jetson Tegra K1 and also the integration of these boards with a mobile robot, for tracking in cluttered environment, where the velocity control of the robot is achieved using MPC.
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Registration and Tracking by using Mean Shift Algorithm
Kavya G,Jharna Majumdar,M Tech +2 more
- 01 Jan 2014
TL;DR: In this paper, mean shift algorithm which is an iterative method, efficient approach to track a non rigid object is proposed, three different kernels are used for weight distribution, namely Uniform, Gaussian, Epanechnikov kernel and Bhattacharyya coefficient is used.
References
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Ofir Pele,Michael Werman +1 more
- 05 Sep 2010
TL;DR: It is shown that the new QC members outperform state of the art distances for these tasks, while having a short running time, and the experimental results show that both the cross-bin property and the normalization are important.
Evaluation of similarity measurement for image retrieval
Dengsheng Zhang,Guojun Lu +1 more
- 01 Jan 2003
TL;DR: A number of commonly used similarity measurements are described and evaluated in this paper and show that city block distance and /spl chi//sup 2/ Statistics measure outperform other distance measure in terms of both retrieval accuracy and retrieval efficiency.
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An efficient fixed-point implementation of residual resampling scheme for high-speed particle filters
TL;DR: A novel low-complexity residual resampling scheme for particle filters is presented, which uses a simple but effective "particle-tagging" method to compensate for a possible error that can be caused by finite-precision quantization in the resampled step of particle filtering.
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Séverine Dubuisson
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TL;DR: This paper presents a new method for fast histogram computing and its extension to bin to bin histogram distance computing, and shows theoretically and with experimental results the superiority of this approach in many cases.
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