Detection of Moving Object in Dynamic Visual Sequences Based on Partial Least Squares Classifier

Balakumar, Shyamala; Sundaramoorthy, Selvaperumal; Bhoopalan, Ramasubramanian; Prabhakar, G.

doi:10.1007/s10916-019-1386-2

Cited by 1 publication

(1 citation statement)

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“…Dynamic object filtering is of extreme importance to computer vision that need to be solved urgently. Its main challenge is to detect the dynamic content [1][2][3][4] and paint the resulting space left with proper imagery [5][6][7][8]. This is different from the technologies of object segmentation only using semantic information, such as video segmentation and panoptic segmentation.…”

Section: Introductionmentioning

confidence: 99%

A dynamic object filtering approach based on object detection and geometric constraint between frames

Wei

Pan

Gao

et al. 2022

IET Image Processing

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In order to eliminate the influence of moving targets in visual positioning, a dynamic object filtering approach based on object detection and inter-frame geometric constraints is proposed to filter the dynamic objects in monocular images. The object detection algorithm is firstly used to identify and locate objects in the single-frame image, and the object matching between frames is performed. Then, the depth estimation network and pose recovery network are trained jointly to output estimated depth along with transformation matrix of object centroids between frames respectively. Finally, the mapping centroid is obtained from the estimated depth and inter-frame transformation matrix. The joint constraint function is performed to complete the detection and filtering of dynamic objects. The experimental results show that the proposed dynamic object filtering approach not only can filter moving objects in sequence images accurately but also allows to reserve the dynamic objects that are temporarily in the stop state. The generalization ability of this approach is also verified in a real urban road scene and meets the requirements of followup research in visual positioning.

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