Implementation of a tracking Kalman filter on a digital signal processor

Tan, Jiajia; Kyriakopoulos, N.

doi:10.1109/41.3075

Cited by 25 publications

(8 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Threedimensional motion can be handled through repeated use of the two-dimensional system. The problem can be viewed either as an object moving with respect to a sensor or the converse; the two situations are handled through a simple coordinate transformation [4].…”

Section: Problem Formulationmentioning

confidence: 99%

Performance Comparison of Kalman Filter and Mean Shift Algorithm for Object Tracking

Jatoth¹,

Shubhra²,

Ali³

2013

IJIEEB

View full text Add to dashboard Cite

Object tracking is one of the important tasks in the field of computer vision. Some of the areas which need Visual object tracking are surveillance, automated video analysis, etc. Mean shift algorithm is one of the popular techniques for this task and is advantageous when compared to some of the other tracking methods. But this method would not be appropriate in the case of large target appearance changes and occlusion. In addition, this method fails when the object is under the action of non-linear forces like that of the gravity e.g. a ball falling under the action of gravity. Another popular method used for tracking is the one that uses Kalman filter, with measurements (often noisy) of position of object to be tracked as input to it. This paper is based on a simulative comparison of both of these algorithms which will give a proper outline of which method will be more appropriate for object tracking, given the nature of motion of object and type of surroundings. Observations based on these methods are present in the literature but there is no evidence based on implementation of these algorithms that shows a quantitative comparison of the said algorithms

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Performance Comparison of Kalman Filter and Mean Shift Algorithm for Object Tracking

Jatoth¹,

Shubhra²,

Ali³

2013

IJIEEB

View full text Add to dashboard Cite

show abstract

“…In the case of a moving obstacle, it is the information that can help predict the future route. When applying the Kalman Filter, p and 0 are estimated respectively for a low-performance processor and to shorten the time in executing the program [11]. Fig.…”

Section: Estimation Of Position Of a Moving Objectmentioning

confidence: 99%

“…To determine the velocity and angular velocity for the robot to avoid an obstacle, the boundary value, the distance to the obstacle, and the robot's maximum velocity and angular velocity were applied using the following expression in the nearness diagram algorithm of Minguez and Montano [8]. V dobs, min dsafe C)O = X Wmax- (11) (12) 2 Here, vo represents the robot's velocity depending on the approach of the obstacle, dobs the distance between the robot and obstacle, dsafe the boundary value to avoid the obstacle, cOo the robot's angular velocity depending on the obstacle position, and 0 the direction in which the robot must move. (11) and (12), the velocity of the autonomous mobile robot is determined by the direction and distance to the obstacle.…”

Section: Autonomous Mobile Robot Determines the Moving Directionmentioning

confidence: 99%

Moving Obstacle Avoidance Using a LRF Sensor

Sohn

Hong

2006

2006 SICE-ICASE International Joint Conference

View full text Add to dashboard Cite

In this paper, an obstacle avoidance algorithm using a laser range finder (LRF) sensor for a mobile manipulator is investigated. A moving obstacle is regarded as a moving circle and therefore the obstacle detection problem becomes the problem of finding the center of a moving circle and its radius. To estimate the position and velocity of the obstacle, Kalman filter is designed. Using velocity and angular velocity of obstacle, we consider that the obstacle move straight. So we forecast a collision of robot and obstacle. Experimental results are provided.

show abstract

“…Thus, the process of object tracking is an estimation process. Several estimation techniques such as Kalman filters [45], Bayesian estimation [46], and Kernel particle filtering [47] have been studied for object detection and tracking.…”

Section: Application-specific Examplementioning

confidence: 99%

Hierarchical Aggregation and Intelligent Monitoring and Control in Fault-Tolerant Wireless Sensor Networks

Sridhar

Madni

Jamshidi

2007

IEEE Systems Journal

View full text Add to dashboard Cite

The primary idea behind deploying sensor networks is to utilize the distributed sensing capability provided by tiny, low powered and low cost devices. Multiple sensing devices can be used cooperatively and collaboratively to capture events or monitor space more effectively than a single sensing device. The realm of applications envisioned for sensor networks is diverse including military, aerospace, industrial, commercial, environmental and health monitoring. Typical examples include: traffic monitoring of vehicles, crossborder infiltration-detection and assessment, military reconnaissance and surveillance, target tracking, habitat monitoring and structure monitoring, to name a few. Most of the applications envisioned with sensor networks demand highly reliable, accurate and fault-tolerant data acquisition process. The integrity of data alone can have tremendous effects on the performance of any data acquisition system. Due to the low manufacturing cost, the sensors lend themselves to be deployed in large numbers with a high spatial distribution. Such a large deployment scheme often generates enormous amount of data that needs to be efficiently summarized and delivered for analysis and ix TABLE OF CONTENTS LIST OF FIGURES .

show abstract

Implementation of a tracking Kalman filter on a digital signal processor

Cited by 25 publications

References 2 publications

Performance Comparison of Kalman Filter and Mean Shift Algorithm for Object Tracking

Performance Comparison of Kalman Filter and Mean Shift Algorithm for Object Tracking

Moving Obstacle Avoidance Using a LRF Sensor

Hierarchical Aggregation and Intelligent Monitoring and Control in Fault-Tolerant Wireless Sensor Networks

Contact Info

Product

Resources

About