Improving the Performance of Video Tracking Using SVM

Gorantla, Anusha; Julie, E. Golden

doi:10.14445/22315381/ijett-v11p226

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The encounters in tracking the objects may instigate due to the fast object motion, (Anusha and Julie, 2014) varying appearance patterns of both the object and the scene, non-rigid object edifices, object-to-object and object-to-scene occlusions, and the camera motion. Usually, bottom-up and top-down methods are two types of methodologies for the visual tracking issue (Hu et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Occlusion reasoning for object tracking is one among the most stimulating issues in visual surveillance (Ning et al, 2002). Furthermore, The SIFT that characterises an appropriate feature is an advanced method for recognising and extracting local feature descriptors that are logically invariant to adjustments in illumination, scaling, rotation, image noise and minor modification in the perspective (Anusha and Julie, 2014). In object tracking, assured features establish the colour, edge, position, moment and so on.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Object Tracking by Employing Shaped Based Features and Kalman Filter

Philip

Mukesh

2017

IJBIDM

View full text Add to dashboard Cite

There has been fast development happening in the multimedia and the related technologies, particularly associated with visual tracking and search operations. Moving target detection has been comprehensively engaged in various arenas but has the disadvantage that the scheme is frequently complex and also that tracking is affected numerous external factors. In this article, multiple objects recognition and tracking is projected so as to progress the method and make it more robust and general with assistance of shape-based features and Kalman filter. Primarily, the input video is rehabilitated to frames and then manually segmented for object segmentation. Consequently, the objects are tracked with the help of Kalman filtering. The method is assessed under standard evaluation metrics of error value and the score value. The technique achieved maximum score values of 95% and minimum error value of 25%. The results validate the effectiveness of the technique.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%