Analysis and characterization of embedded vision systems for taxonomy formulation

Abstract

The current trend in embedded vision systems is to propose bespoke solutions for specific problems as each application has different requirement and constraints. There is no widely used model or benchmark which aims to facilitate generic solutions in embedded vision systems. Providing such model is a challenging task due to the wide number of use cases, environmental factors, and available technologies. However, common characteristics can be identified to propose an abstract model. Indeed, the majority of vision applications focus on the detection, analysis and recognition of objects. These tasks can be reduced to vision functions which can be used to characterize the vision systems. In this paper, we present the results of a thorough analysis of a large number of different types of vision systems. This analysis led us to the development of a system's taxonomy, in which a number of vision functions as well as their combination characterize embedded vision systems. To illustrate the use of this taxonomy, we have tested it against a real vision system that detects magnetic particles in a flowing liquid to predict and avoid critical machinery failure. The proposed taxonomy is evaluated by using a quantitative parameter which shows that it covers 95 percent of the investigated vision systems and its flow is ordered for 60 percent systems. This taxonomy will serve as a tool for classification and comparison of systems and will enable the researchers to propose generic and efficient solutions for same class of systems.

Complexity Analysis of Vision Functions for Comparison of Wireless Smart Cameras

“…The taxonomy may not be exhaustive, but it does cover both the fundamental and the common vision functions which are required for a wide range of VSN vision systems. The study [12] showed that our proposed taxonomy covers 95 percent of the investigated systems. After the system taxonomy, the complexity in terms of arithmetic operations and memory of vision functions are investigated for the complexity model.…”

“…It is worth mentioning that for some tasks there are a number of algorithms, each with varying complexity. In this paper, we have investigated the complexity of functions that are commonly used in wireless smart camera systems [12]. However, in this case, the system taxonomy needs to be updated periodically in order to make it more exhaustive for the current systems.…”

STC-CAM1, IR-visual based smart camera system

“…In this architecture, data intensive tasks are performed on the camera node whereas the high level tasks e.g., feature extraction, identification and classification are performed on the client device. This makes architecture generic because pre-processing are same for many surveillance applications [5]. STC-CAM1 consists of two modules, in-node processing module and a communication module.…”

“…These advancements facilitate development of stand-alone, scalable, easy-to-integrate, low cost, and low power solutions for a number of potential applications. Typical applications include surveillance for safetycritical and hard real-time systems, health care, machine vision and smart home systems [3]- [5]. This paper focuses on safety critical applications, where visual sensors alone are not enough for satisfactory surveillance, because vision sensors are sensitive to visible range of the electromagnetic spectrum.…”

A Taxonomy of Vision Systems for Ground Mobile Robots

“…The use of vision sensors can be classified according to different criteria ( [17], [18]), but we here focus only on the most widely used vision sensors, i.e, cameras. A camera is an optical instrument that records images that are stored and/or transmitted to another location.…”