A tool for vision based pedestrian detection performance evaluation

This study presents a novel learning-based pedestrian detection system capable of automatically detecting individuals of different sizes and orientations against a wide variety of backgrounds, including crowds, even when the individual is partially occluded. To render the detection performance robust toward the effects of geometric and rotational variations in the original image, the feature extraction process is performed using both rectangular- and circular-type blocks of various sizes and aspect ratios. The extracted blocks are rotated in accordance with their dominant orientation(s) such that all the blocks extracted from the input images are rotationally invariant. The pixels within the cells in each block are then voted into rectangular- and circular-type 9-bin histograms of oriented gradients (HOGs) in accordance with their gradient magnitudes and corresponding multivariate Gaussian-weighted windows. Finally, four cell-based histograms are concatenated using a tri-linear interpolation technique to form one 36-dimensional normalized HOG feature vector for each block. The experimental results show that the use of the Gaussian-weighted window approach and tri-linear interpolation technique in constructing the HOG feature vectors improves the detection performance from 91% to 94.5%. In the proposed scheme, the detection process is performed using a cascaded detector structure in which the weak classifiers and corresponding weights of each stage are established using the AdaBoost self-learning algorithm. The experimental results reveal that the cascaded structure not only provides a better detection performance than many of the schemes presented in the literature, but also achieves a significant reduction in the computational time required to classify each input image.

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Performance evaluation of FIR sensor systems applied to pedestrian detection

10.1117/12.850150 ◽

2010 ◽

Author(s):

S. Franz ◽

R. Schweiger ◽

O. Loehlein ◽

D. Willersinn ◽

K. Kroschel

Keyword(s):

Performance Evaluation ◽

Pedestrian Detection ◽

Sensor Systems

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Pedestrian Detection by Novel Axis-Line Representation and Regression Pattern

Sensors ◽

10.3390/s21103312 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3312

Author(s):

Mengxue Zhang ◽

Qiong Liu

Keyword(s):

Pedestrian Detection ◽

Transformation Method ◽

Detection Performance ◽

Experimental Results ◽

Background Information ◽

Object Representations ◽

Network Training ◽

Regression Pattern ◽

Initialization Strategy ◽

Regression Target

The pattern of bounding box representation and regression has long been dominant in CNN-based pedestrian detectors. Despite the method’s success, it cannot accurately represent location, and introduces unnecessary background information, while pedestrian features are mainly located in axis-line areas. Other object representations, such as corner-pairs, are not easy to obtain by regression because the corners are far from the axis-line and are greatly affected by background features. In this paper, we propose a novel detection pattern, named Axis-line Representation and Regression (ALR), for pedestrian detection in road scenes. Specifically, we design a 3-d axis-line representation for pedestrians and use it as the regression target during network training. A line-box transformation method is also proposed to fit the widely used box-annotations. Meanwhile, we explore the influence of deformable convolution base-offset on detection performance and propose a base-offset initialization strategy to further promote the gain brought by ALR. Notably, the proposed ALR pattern can be introduced into both anchor-based and anchor-free frameworks. We validate the effectiveness of ALR on the Caltech-USA and CityPersons datasets. Experimental results show that our approach outperforms the baseline significantly through simple modifications and achieves competitive accuracy with other methods without bells and whistles.

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