Speedy Object Detection based on Shape

TL;DR

This paper presents a shape-based, scalable, and efficient object detection method designed for real-time audio-based assistance for visually impaired users, emphasizing speed and minimal feature spaces.

Contribution

It introduces a novel shape and scale-based clustering approach for rapid object detection, reducing processing time compared to traditional methods.

Findings

Achieves faster detection by clustering objects based on shape and scale.

Reduces feature space for quicker processing.

Effectively identifies new objects when no match is found.

Abstract

This study is a part of design of an audio system for in-house object detection system for visually impaired, low vision personnel by birth or by an accident or due to old age. The input of the system will be scene and output as audio. Alert facility is provided based on severity levels of the objects (snake, broke glass etc) and also during difficulties. The study proposed techniques to provide speedy detection of objects based on shapes and its scale. Features are extraction to have minimum spaces using dynamic scaling. From a scene, clusters of objects are formed based on the scale and shape. Searching is performed among the clusters initially based on the shape, scale, mean cluster value and index of object(s). The minimum operation to detect the possible shape of the object is performed. In case the object does not have a likely matching shape, scale etc, then the several operations required for an object detection will not perform; instead, it will declared as a new object. In such way, this study finds a speedy way of detecting objects.