A Pedestrian Tracking Algorithm Based on Mean Shift Using Color Histogram Equalization Method

2017 ◽  
Author(s):  
Limei Fu ◽  
Shuhao Yu ◽  
Yue Jia
Keyword(s):  
Mean Shift ◽  
Histogram Equalization ◽  
Tracking Algorithm ◽  
Color Histogram ◽  
Pedestrian Tracking ◽  
Equalization Method

Mean-Shift tracking algorithm based on FCM

2010 ◽  
Vol 29 (12) ◽  
pp. 3332-3335 ◽  
Author(s):  
Cun-wei TIAN ◽  
Guang-ying GE ◽  
Zhe SHEN
Keyword(s):  
Mean Shift ◽  
Tracking Algorithm

scholarly journals Smartphone-Based Inertial Odometry for Blind Walkers

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4033
Author(s):  
Peng Ren ◽  
Fatemeh Elyasi ◽  
Roberto Manduchi
Keyword(s):  
Particle Filtering ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Mean Shift ◽  
Gait Pattern ◽  
Comparative Assessment ◽  
Pedestrian Tracking ◽  
Step Counter ◽  
Assisted Navigation

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

scholarly journals Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Hui Li ◽  
Yun Liu ◽  
Chuanxu Wang ◽  
Shujun Zhang ◽  
Xuehong Cui
Keyword(s):  
Particle Filters ◽  
A Priori ◽  
Texture Features ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Particle State ◽  
Pedestrian Tracking ◽  
Estimation Problems ◽  
Non Gaussian ◽  
Gaussian Estimation

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.

scholarly journals Segmentation of Multi-Tumor from PET/CT Images

2019 ◽  
Vol 8 (12S2) ◽  
pp. 170-176
Keyword(s):  
Automatic Segmentation ◽  
Mean Shift ◽  
Ground Truth ◽  
Histogram Equalization ◽  
Ct Images ◽  
Svm Classifier ◽  
Segmentation Evaluation ◽  
Multiple Tumor ◽  
Mean Shift Clustering ◽  
Pet Ct

The aim of the project is to develop a methodology for automatic segmentation of multiple tumor from PET/CT images. Image pre-processing methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE), image sharpening and Wiener filtering were performed to remove the artifacts due to contrast variations and noise. The image was segmented using K-means, Threshold segmentation, watershed segmentation, FCM clustering Segmentation, Mean shift Clustering Segmentation, Graph Cut Segmentation. Evaluation was made for the segmentation against the Ground Truth. Various Features was selected and extracted. Classification was made using SVM classifier and KNN classifier to classify the tumor as benign or malignant. The proposed method was carried out using PET/CT images of lung cancer patients and implemented using MATLAB.

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