Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

Ortiz, Valentina Paz Henríquez; Catala, Omar Del Tejo; Igual, Ismael Salvador; Pérez-Cortés, Juan-Carlos

doi:10.1177/1729881420929175

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…From text analysis [1], [2] or pedestrian detection [3], [4] to healthcare [5], [6], it is unquestionable that Artificial Intelligence (AI) has become more and more useful in almost…”

Section: Introductionmentioning

confidence: 99%

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

et al. 2022

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The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm's accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.

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“…From text analysis [1], [2] or pedestrian detection [3], [4] to healthcare [5], [6], it is unquestionable that Artificial Intelligence (AI) has become more and more useful in almost…”

Section: Introductionmentioning

confidence: 99%

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

et al. 2022

Self Cite

View full text Add to dashboard Cite

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“…Prominent examples for real-time object detection and analysis include Google Lens or smart city applications that perform video surveillance [3][4][5] or for connected autonomous cars, as illustrated in Figure 1. Especially for the latter, incorporating new sensor data such as from LIDAR and other on-board sensors that goes beyond image data alone is also attracting interest [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

2021

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With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.

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“…YOLO algorithms belong to the one-stage category and are used on on-road applications for object detection including instances such as pedestrians [ 21 , 22 , 23 , 24 ], vehicles [ 25 ], traffic flows [ 26 ], and non-helmeted motorcyclists [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

High-Profile VRU Detection on Resource-Constrained Hardware Using YOLOv3/v4 on BDD100K

et al. 2020

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Vulnerable Road User (VRU) detection is a major application of object detection with the aim of helping reduce accidents in advanced driver-assistance systems and enabling the development of autonomous vehicles. Due to intrinsic complexity present in computer vision and to limitations in processing capacity and bandwidth, this task has not been completely solved nowadays. For these reasons, the well established YOLOv3 net and the new YOLOv4 one are assessed by training them on a huge, recent on-road image dataset (BDD100K), both for VRU and full on-road classes, with a great improvement in terms of detection quality when compared to their MS-COCO-trained generic correspondent models from the authors but with negligible costs in forward pass time. Additionally, some models were retrained when replacing the original Leaky ReLU convolutional activation functions from original YOLO implementation with two cutting-edge activation functions: the self-regularized non-monotonic function (MISH) and its self-gated counterpart (SWISH), with significant improvements with respect to the original activation function detection performance. Additionally, some trials were carried out including recent data augmentation techniques (mosaic and cutmix) and some grid size configurations, with cumulative improvements over the previous results, comprising different performance-throughput trade-offs.

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Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

Cited by 6 publications

References 26 publications

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

High-Profile VRU Detection on Resource-Constrained Hardware Using YOLOv3/v4 on BDD100K

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