Smaller Object Detection for Real-Time Embedded Traffic Flow Estimation Using Fish-Eye Cameras

Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection

“…Estimation algorithms are the basis of tracking algorithms not only for large objects but also for small ones. These algorithms depend on the signal acquisition methods and are used for video systems [7][8][9][10][11][12].…”

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

“…The reviewed AmI literature is good evidence of that. Although the works specifically identified along those lines [64,68,69,[77][78][79]82,85,96,102,105,106] cover only a limited subset of the broad spectrum of applications resulting from the technological progress in the field, they provide good intuition on the relevance of object detection as a key factor for making both vehicles and infrastructures safer, more efficient, comfortable and reliable. In particular, in-vehicle ITS systems [64,[77][78][79]85,96,102,105,106] (commonly known as Advanced Driving Assistance Systems or ADAS) stand out in the analysis as the central focus of interest.…”

“…Such systems embed detection solutions conceived as safety mechanisms for monitoring both driver operations [77,78,85], preventing distractions, and ultimately, the loss of control of the vehicle, as well as on-road events [64,79,96,102,105,106], being the latter mainly implemented nowadays in the form of a warning instrument triggered in situations of potential collisions [79,96] or infractions [106], but also designed towards decision-making support in future autonomous vehicles [64,102,105]. For its part, regarding conventional ITS alternatives [68,69,82], the different approaches identified are aimed at road traffic control and early response to emergencies, applications in which the use of UAVs has been intensively explored [68,69] due to their capabilities to record and communicate information in a non-intrusive way, as well as to recognize areas inaccessible by other means. A key challenge in this context lies in UAVs' built-in computing components since they have to both process the acquired images with almost no latency to make critical navigation decisions [68] while consuming as little power as possible to minimize its impact on the battery life and hence on the system's flight time [69].…”

“…The result is a simplified and smaller architecture, conceived as a single feedforward neural network, suitable to derive high-inference-speed detection models. The unified approach, crystallized in compact architectures considered a standard nowadays, such as SSD [118] and YOLO in its different versions [119][120][121][122], constitute the foundation on which modern lightweight object detection architectures have been built in AmI [48,49,52,54,55,63,65,66,68,70,72,73,75,[77][78][79][80][81][82][86][87][88]90,91,94,100,[105][106][107]. Such compact architectures, however, resulted from an eminently moderate structural optimization in a first attempt to make CNNs more manageable, prioritizing accuracy preservation over structural reduction and inference speed increase.…”

“…In this regard, pre-trained detectors can be either exploited right away or, on the contrary, be tuned for a different second target task (typically, more specific) through transfer learning. This transfer learning approach enables reusing already existing models [48,55,63,70,75,76,[78][79][80][81][82]87,91,100,103,107], previously trained on public standard object detection benchmarks, such as Pascal VOC [115] (used in [48]), and Microsoft COCO [126] (used in [63,75,78,80,91,107]). Still, as shown in Table 2, it is a common practice as well to exploit not the entire detection model but merely the backbone [49,50,59,68,71,95,106,109], being this a CNN embedded in the detection framework, responsible for extracting from some given input images the different feature maps subsequently exploited by the deeper layers of the detector for predicting the several classes and bounding boxes produced as output.…”