Vision-based bicyclist detection and tracking for intelligent vehicles

Abstract

Abstract-This paper presents a vision-based framework for intelligent vehicles to detect and track people riding bicycles in urban traffic environments. To deal with dramatic appearance changes of a bicycle according to different viewpoints as well as nonrigid nature of human appearance, a method is proposed which employs complementary detection and tracking algorithms. In the detection phase, we use multiple view-based detectors: frontal, rear, and right/left side view. For each view detector, a linear Support Vector Machine (SVM) is used for object classification in combination with Histograms of Oriented Gradients (HOG) which is one of the most discriminative features. Furthermore, a real-time enhancement for the detection process is implemented using the Integral Histogram method and a coarse-to-fine cascade approach. Tracking phase is performed by a multiple patch-based Lucas-Kanade tracker. We first run the Harris corner detector over the bounding box which is the result of our detector. Each of the corner points can be a good feature to track and, in consequence, becomes a template of each instance of multiple Lucas-Kanade trackers. To manage the set of patches efficiently, a novel method based on spectral clustering algorithm is proposed. Quantitative experiments have been conducted to show the effectiveness of each component of the proposed framework.

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

“…A Fast R-CNN is a DL approach which will be discussed in later sections. A similar approach for using DPM was found in [52]. Methods for detecting pedestrians can be employed for cyclist detection as in [53,54].…”

Two-wheeler Detection Algorithm Research using Homogeneity Operator and Gamma Correction based on Adaboost Classifier

“…A range of thresholds of -20 to 20 was utilized in classification, and confusion matrix, true positive rate (TPR) and false positive rate (FPR) were used for analyzing experimental results per angles for the methods, and ROC [14] curves are shown in Figure 6, by applying Equation (8) (8) where "TP" is True Positive", "FP" is False Positive", "TN" is True Negative and "FN" is False Negative.…”

“…So two wheelers detection system can be adapted to the pedestrian detection algorithms for feature extraction, classification, and non-maxima suppression. The solution of slow performance from a dense encoding scheme and multi-level scale images is to use a boosting algorithm [14] to speed up classification process. Because of above reasons, we tried to use modified HOG and homogeneity operator algorithm to select best features and Adaboost to improve detection rate.…”

Bicyclist Recognition and Orientation Estimation from On-board Vision System

“…Besides pedestrian detection, detection of other kinds of road users was also performed. Cho et al detected bicyclist using both HOG method (8) and Part-Based Model (9) . Multiple view-based detectors: frontal, rear, and right/left side view, were used in detection.…”