Objective

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Human body detection is a key subject of computer vision and has important research relevance in areas

such as intelligent video surveillance

unmanned driving

and intelligent robots. Head-shoulder detection is often used in embedded systems due to its strong anti-masking capabilities

attitude adaptability

and low computational requirements. Commonly used embedded head-shoulder detection methods mainly include motion detection and matching; however

these two methods have low detection accuracy and poor adaptability to different postures and human appearances. To improve the head-shoulder detection accuracy

an embedded real-time human head-shoulder detection method based on aggregated channel features (ACFs) is proposed.

Method

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A variety of pedestrian detection and human pose datasets

namely

Caltech Pedestrian dataset

INRIA Pedestrian dataset

and MPⅡ Human Pose dataset

are analyzed to generate human head-shoulder sample. Suitable samples in MPⅡ Human Pose dataset are filtered. Then

head-shoulder areas are clipped accurately on the basis of the positions of head and neck joints

and a human head-shoulder dataset with varied head-shoulder poses and perspectives

named MPⅡ-HS

is generated. The MPⅡ-HS dataset is used as positive training samples. Images from Caltech and INRIA Pedestrian datasets

which do not contain humans are used as negative training samples. AdaBoost algorithm with multiple stages

which consist of one channel of gradient amplitude

six channels of gradient direction

and three channels in YUV color space

is used to train a head-shoulder classifier for a 40×40 pixels image based on ACFs. The trained classifier is an enhanced decision tree composed of 4 096 binary decision trees with a maximum depth of five. The final score of the classifier is the sum of the scores of every binary decision tree. The classification will end early if the score sum reaches a lower threshold to speed up detection. Image feature pyramid is calculated based on fast feature pyramid algorithm. For the Linux ARM platform

multi-core parallel techniques and single-instruction multiple-data instruction set are used to accelerate the calculation of image feature pyramid. Finally

sliding-window detection is applied in multiple threads where each thread handles one row of detection windows. The trained head-shoulder image classifier identifies candidate head-shoulder targets in every detection window

and candidate detection results are merged via non-maximum suppression algorithm.

Result

2

To estimate the accuracy of the proposed head-shoulder detector

head-shoulder targets in the validation set of INRIA Pedestrian dataset are re-labeled and named as INRIA-HS. The trained head-shoulder image classifier is applied. The detection results are evaluated by miss rate (MR) and false positive per picture (FPPI) in the receiver operating characteristic curve. The log-average MS for head-shoulder targets with a height of ≥ 50 pixels in INRIA-HS dataset is 16.61%

that of MR is lower than 20%

whereas the FPPI is 0.1. In addition

the head-shoulder images of various poses and perspectives in different scenes are collected in actual scenes to verify the adaptability of the proposed classifier. Results show that the proposed classifier can detect multi-pose

multi-perspective

and occluded head-shoulder target under different illumination conditions. However

the receptive field of the proposed classifier is limited to the head-shoulder area; thus

some image areas similar to that of the head-shoulder but not similar to the human body may be misclassified as positive. Thus

the FPPI of the proposed head-shoulder detection is slightly higher than that of the ACF classifier trained for all human body detection. However

the proposed head-shoulder classifier is suitable for occluded human in indoor and crowded scenes. In the embedded platform with quad-core ARM Cortex-A53 with 1.4 GHz Raspberry Pi 3B

the proposed optimized head-shoulder detection program takes approximately 178 ms for a 640×480 pixels image. For a single detection window containing positive samples

the classification takes approximately 2 ms. The overall detection speed can satisfy the demands of real-time detection of video streams.

Conclusion

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Human head-shoulders are detected based on ACF. The generated head-shoulder dataset MPⅡ-HS has rich and varied head-shoulder samples with accurate annotations. The AdaBoost algorithm is used to learn the ACF of head-shoulder images. The trained head-shoulder image classifier has strong adaptability to different human poses or appearances. It benefits from the struct of classifier

and its hardware performance requirement is low. These advantages allow human head-shoulder detection accuracy possible on an embedded platform in a wide range.