Real Time Object Detection for Traffic Based on Knowledge Distillation: 3^rd Place Solution to Pair Competition

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

“…It is possible to create a first classification in which we can distinguish, on the one hand a number of general-scope works [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66] and on the other hand a larger group of publications that present domain-specific research . The first group includes studies that, far from focusing on a particular application area, propose more open research involved in the search for efficient and lightweight detection solutions, oriented to AmI scenarios.…”

“…The first group includes studies that, far from focusing on a particular application area, propose more open research involved in the search for efficient and lightweight detection solutions, oriented to AmI scenarios. Overall, authors pursue advances in object detection within the ondevice paradigm, trying, in general terms, to satisfy the requirements that the implementation or deployment of state-of-the-art detection techniques in resource-limited devices inevitably entail, but putting particular interest in, if not achieving real-time performance [49,50,54,64,66], obtaining at least an adequate trade-off between speed and accuracy [52,56,57,60,65]. The experimentation proposed along those lines is typically contextualized in a given AmI scenario or application.…”

“…It is used instead for practical purposes, just as a use case for quantitatively analyzing the performance of the algorithms and methods devised, as well as for assessing the feasibility of their deployment given the constraints of the hardware platforms used; merely having implications on the datasets (on the classes or object types considered) exploited for training, fine-tuning, and testing the CNNs designed. There exists a prevalence in that regard, of widespread and cross-domain AmI applications on mobile or embedded devices, such as face detection [48,58,[60][61][62] (biometric security, surveillance), and vehicle and pedestrian detection (security, surveillance, autonomous vehicles, smart cities) both in cars [56,64,65] and unmanned aerial vehicles (UAV) [49,[52][53][54][55]57,59]. [73,74,88,97,110] and smart cities [72,100,101,108], all of them, scenarios where constant and real-time object detection is necessary for enabling context-awareness on end devices.…”

“…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.…”

“…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].…”