基于DSP的通过局部特征实时物体识别嵌入式系统x文档格式.docx

1、#附录 A#Real-time object recognition using local features on a DSP-based embedded system#Abstract#In the last few years, object recognition has become one of the most popular tasks in computer vision. In particular, this was driven by the development of new powerful algorithms for local appearance bas

2、ed object recognition. So-called smart cameras with enough power for decentralized image processing became more and more popular for all kinds of tasks, especially in the field of surveillance. Recognition is a very important tool as the robust recognition of suspicious vehicles, persons or objects

3、is a matter of public safety. This simply makes the deployment of recognition capabilities on embedded platforms necessary. In our work we investigate the task of object recognition based on state-of-the-art algorithms in the context of a DSP-based embedded system. We implement several powerful algo

4、rithms for object recognition, namely an interest point detector together with an region descriptor, and build a medium-sized object database based on a vocabulary tree, which is suitable for our dedicated hardware setup. We carefully investigate the parameters of the algorithm with respect to the p

5、erformance on the embedded platform. We show that state-of-the- art object recognition algorithms can be successfully deployed on nowadays smart cameras, even with strictly limited computational and memory resources.#KeywordsDSP ;# Object recognition;# Local features;# Vocabulary tree#1. Introductio

6、n#Object recognition is one of the most popular tasks in the field of computer vision. In the past decade, big efforts were made to build robust object recognition systems based on appearance features with local extent. For such a framework to be applicable in the real world several attributes are v

7、ery important:# insensitivity against rotation, illumination or view point changes, as well as real-time behavior and large- scale operation. Current systems already have a lot of these properties and, though not all problems have been solved yet, nowadays they become more and more attractive to the

8、 industry for inclusion in products for the customer market.#In turn, recently embedded vision platforms such as smart cameras have successfully emerged, however, only offering a limited amount of computational and memory resources. Nevertheless, embedded vision systems are already present in our ev

9、eryday life. Almost everyones mobile phone is equipped with a camera and, thus, can be treated as a small embedded vision system. Clearly this gives rise to new applications, like navigation tools for visually impaired persons, or collaborative public monitoring using millions of artificial eyes. In

10、 addition, the low price of digital sensors and the increased need for security in public places has led to a tremendous growth in the number of cameras mounted for surveillance purposes. They have to be small in size and have to process the huge amounts of available data on site. Furthermore, they

11、have to perform dedicated operations automatically and without human interaction. Not only in the field of surveillance, but also in the areas of household robotics, entertainment, military and industrial robotics, embedded computer vision platforms are becoming more and more popular due to their ro

12、bustness against environmental adversities. Especially DSP-based embedded platforms are very popular as they are powerful and cheap CPUs, which are still small in size and efficient in terms of power consumption. As DSP offer the maximum in flexibility of the software to be run, compared to other em

13、bedded units like FPG As, ASIC or GPU, their current success is not surprising.#For the reasons already mentioned, recognition tasks are a very important area of research. However, in this respect some attributes of embedded platforms strictly limit the practicability of current state-of-the-art app

14、roaches. For example, the amount of memory available on a device strictly limits the number of objects in the database. Therefore, for building an embedded object recognition system, one goal is to make the amount of data to represent a single object as small as possible in order to maximize the num

15、ber of recognizable objects. Another important aspect is the real- time capability of these systems. Algorithms have to be fast enough to be operational in the real world. They have to be robust and user-friendly;# otherwise, a product equipped with such functionality is simply unattractive to a pot

16、ential customer. For example, in an interactive tour through a museum, object recognition on a mobile device has to be fast enough to allow for continuity in guidance. Formally speaking, we consider this to be an application requiring soft real-time system behavior. Clearly, this is just one example, and the exact meaning of the term real-time is dependent on the concrete application. We still consider an object recognition system as being real-#time capable, if it is able t