Significant progress has been achieved over the past decade on vision-based pedestrian detection; this has led to active pedestrian safety systems being deployed in most mid- to high-range cars on the market. Comparatively little effort has been spent on vision-based cyclist detection, especially when it concerns quantitative performance analysis on large datasets. We present a large-scale experimental study on cyclist detection where we examine the currently most promising object detection methods; we consider Aggregated Channel Features, Deformable Part Models and Region-based Convolutional Neural Networks. We also introduce a new method called Stereo-Proposal based Fast R-CNN (SP-FRCN) to detect cyclists based on stereo proposals and Fast R-CNN (FRCN) framework. Experiments are performed on a dataset containing 22161 annotated cyclist instances in over 30000 images, recorded from a moving vehicle in the urban traffic of Beijing. Results indicate that all the three solution families can reach top performance around 0.89 average precision on the easy case, but the performance drops gradually with the difficulty increasing. The dataset including rich annotations, stereo images and evaluation scripts (termed “Tsinghua-Daimler Cyclist Benchmark”) is made public to the scientific community, to serve as a common point of reference for future research.

A new benchmark for vision-based cyclist detection

Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.

A Unified Framework for Concurrent Pedestrian and Cyclist Detection

In this article, a unified joint detection framework for pedestrian and cyclist is established to realize the joint detection of pedestrian and cyclist targets. Based on the target detection of fas...

https://journals.sagepub.com/doi/pdf/10.1177/1729881419829651

Pedestrian and cyclist detection based on deep neural network fast R-CNN:

The invention discloses a pedestrian detection method and a pedestrian detection device The method comprises the following steps of: collecting a pedestrian image, preprocessing the image, and extracting a region of interest from the preprocessed image; pre-positioning a pedestrian region in the region of interest by adopting an Adaboost cascade classifier; and positioning the pedestrian region in the pedestrian region pre-positioned by the Adaboost cascade classifier by adopting a support vector machine (SVM) By the technical scheme, the technical problem that the fallout ratio in the prior art is high

Pedestrian detection method and device

Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an important role in future traffic. To avoid accidents and achieve a highly efficient traffic flow, it is important to detect VRUs and to predict their intentions. In this article a holistic approach for detecting intentions of VRUs by cooperative methods is presented. The intention detection consists of basic movement primitive prediction, e.g. standing, moving, turning, and a forecast of the future trajectory. Vehicles equipped with sensors, data processing systems and communication abilities, referred to as intelligent vehicles, acquire and maintain a local model of their surrounding traffic environment, e.g. crossing cyclists. Heterogeneous, open sets of agents (cooperating and interacting vehicles, infrastructure, e.g. cameras and laser scanners, and VRUs equipped with smart devices and body-worn sensors) exchange information forming a multi-modal sensor system with the goal to reliably and robustly detect VRUs and their intentions under consideration of real time requirements and uncertainties. The resulting model allows to extend the perceptual horizon of the individual agent beyond their own sensory capabilities, enabling a longer forecast horizon. Concealments, implausibilities and inconsistencies are resolved by the collective intelligence of cooperating agents. Novel techniques of signal processing and modelling in combination with analytical and learning based approaches of pattern and activity recognition are used for detection, as well as intention prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge fusion, takes place on the level of perception and intention recognition. Based on the requirements of the cooperative approach for the communication a new strategy for an ad hoc network is proposed.

Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence.

Vision based cyclist detection is a new application in the field of intelligent transportation. Compared with pedestrian detection, this new problem is more challenging because various appearence and motion of bicycles increase the diversity of the detection objects; therefore existing pedestrian detection approaches can hardly get good overall performance because cyclist detection requires more information represented by more effective features to enable detection. For general object detection and pedestrian detection, histogram of oriented gradient (HOG) features achieved great success; however it have two major drawbacks: time-consuming caused by dense/overlap sampling and only local information is retained. In this paper, we proposed a more effective feature extraction method (i.e., HOG-LP) to overcome the drawbacks of general HOG feature extraction for crossing cyclist detection. On one hand, an improved light/non-overlap sampling method is proposed to speed up HOG feature extraction; on the other hand, pyramid sampling is utilized to extract additional global features in different scale spaces in order to retain more information for high classification accuray. With efficient feature extraction, a linear SVM classifier is used to further increase the detection speed. The experimental results tested on urban traffic videos show the effectiveness of the proposed method on crossing cyclist detection.

An effective crossing cyclist detection on a moving vehicle

The invention provides vehicle-mounted pedestrian detection method and system. The method comprises the following steps of: carrying out the classifier training on a computer by utilizing a pedestrian image positive-negative sample library; and carrying out the real-time pedestrian detection on video images acquired by a vehicle-mounted CCD camera on a DSP processor by utilizing the classifier. In the invention, the video image in front of the automobile is acquired by the installed CCD camera on a running automobile, and the pedestrian in the image is detected in real time by a trained classifier, thereby realizing the real-time detection and supporting the safety protection of pedestrians on the intelligent traffic background.

Vehicle-mounted pedestrian detection method and system

The invention discloses a virus evolutionary genetic algorithm based selective ensemble pedestrian detection method. The virus evolutionary genetic algorithm based selective ensemble pedestrian detection method comprises the following steps: training individual classifiers different from each other by an original training set; initializing a main population of a genetic algorithm and an extra virus population; performing an iterative process of the genetic algorithm; and generating a final combined classifier by an optimal individual obtained by the iterative process of the genetic algorithm. By a selective ensemble manner, selection of an optimal classifier combination by iteration of the genetic algorithm and introduction of the extra virus population, the virus evolutionary genetic algorithm based selective ensemble pedestrian detection method has enhanced search capability of the genetic algorithm, avoiding problems of premature convergence and the like, improving the selection effect and improving the detection speed and detection accuracy.

Virus evolutionary algorithm based selective ensemble pedestrian detection method

The invention provides a clustering method and a device. The method comprises the following steps of: selecting clustering centers in a data set and determining a characteristic point set for each clustering center; dividing the data in the data set into the clustering according to the characteristic point sets; determining a new characteristic point set according to the relationship between the data which is divided into the clustering and the characteristic point set; and determining clustering result according to the new characteristic point set. The clustering method and the device are insensitive to the data set and do not increase algorithm complexity.

Clustering method and device

A device for detecting vehicles by overlooking comprises a translation unit, a statistical unit, a calculation unit and a classification unit; wherein, the translation unit is used for translating a first frame vehicle image and an adjacent second frame vehicle image thereof to coincide in a world coordinate system; the statistical unit is used for respectively obtaining the candidate areas of thevehicles in the first frame vehicle image and the second frame vehicle image; the calculation unit is used for carrying out image difference on the candidate areas of the vehicles and pixel areas correspondingly coinciding with the candidate areas to obtain the candidate areas of the moving vehicles in the second frame vehicle image relative to the first frame vehicle image; the classification unit is used for classifying the candidate areas of the moving vehicles according to a classifier obtained through specimen training to detect the areas of the moving vehicles in the second frame vehicle image relative to the first frame vehicle image. The device has high detection rate and low misstatement rate under the premise of ensuring the requirement of real-time.

Device and method for detecting vehicles by overlooking

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