Vehicles Detection for Smart Roads Applications on Board of Smart Cameras: A Comparative Analysis

Video analytics can be profitably adopted in smart roads environments to automatically detect abnormal situations. Within this context, vehicle detection is the first and foremost stage, and its accuracy is crucial, since any detection error will affect the performance of any subsequent step. Furthermore, in smart road environments it is often preferred to perform the video analysis directly on board of smart surveillance cameras, in order to reduce bandwidth usage and eliminate the cost of setup and maintenance of powerful processing servers; on the flip side, processing on board of smart cameras implies the detection algorithm to be fast and slim, since the resources available on this kind of embedded device are limited. In the era of deep learning, it seems that the question what is the best method for vehicle detection? may have a trivial answer, since this class of methods includes some very accurate ones. Anyway, according to the above consideration, the best suited method for this application is not necessarily the most accurate one, but for sure the most accurate one running on the available hardware at a given resolution and frame rate. Starting from the above considerations, in this paper we perform an analysis of the methods available in the literature for vehicle detection, by comparing them in terms of accuracy and computational burden, with the aim to answer the following question: what is the best method for vehicles detection when working with smart cameras?

Automated summary

The translated text discusses the profitable adoption of video analytics in smart road environments and the importance of accurate vehicle detection. It also highlights the preference for performing video analysis directly on smart surveillance cameras to reduce bandwidth usage and the challenge of limited resources for processing on embedded devices. The text raises the question of the best method for vehicle detection in smart camera systems, taking into account accuracy, computational burden, and hardware limitations.