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Repository Details

Active contour model, also called snakes, is a framework in computer vision for delineating an object outline from a possibly noisy 2D image. The snakes model is popular in computer vision, and snakes are greatly used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching.

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The motto of the project is to gain experience in the implementation of different robotic algorithms using ROS framework. The first step of task is to build a map of the environment and navigate to a desired location in the map. Next, we have to sense the location of marker (e.g. AR marker, color markers etc) in the map, where there is pick and place task, and autonomously localise and navigate to the desired marker location. After reaching to the desired marker location, we have to precisely move towards the specified location based on visual servoing. At the desired location, we have a robotic arm which picks an object (e.g a small cube) and places on our turtlebot (called as pick and place task). After, the pick and place task, again the robot needs to find another marker, which specifies the final target location, and autonomously localise and navigate to the desired marker location, which finishes the complete task of the project.
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Human-Activity-Recognition-from-Videos-Using-Machine-Learning

Nowadays, it’s a very hot topic on video-based human action detection, which has recently been demonstrated to be very useful in a wide range of applications including video surveillance, tele-monitoring of patients and senior people, medical diagnosis and training, video content analysis and search, and intelligent human computer interaction [1]. As video camera sensors become less expensive, this approach is increasingly attractive since it is low cost and can be adapted to different video scenarios.
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Mean-Shift (MS) Mean-Shift (MS) is widely known as one of the most basic yet powerful tracking algorithms. Mean- Shift considers feature space as an empirical probability density function (pdf). If the input is a set of points then MS considers them as sampled from the underlying pdf. If dense regions (or clusters) are present in the feature space, then they correspond to the local maxima of the pdf. For each data point, MS associates it with the nearby peak of the pdf As an example, you can see the car sequence in file “Mean_Shift_Tracking.m”. We want to track the car in this sequence. We first needed to define the initial patch of the car in the first frame of the sequence. And then the moving car patch will be estimated by using the Bhattacharya coefficient and the weights corresponding to the neighboring patches. It will be deeply explained in the report.
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10

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11

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12

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13

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14

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15

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16

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17

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18

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19

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20

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22

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