Head Detection and Tracking for Monitoring Human Behaviors

Mae, Y.; Sasao, Naoki; Sakaguchi, Yoshiyuki; Inoue, Keita; Arai, Tsuyoshi

doi:10.1016/b978-044451813-2/50037-3

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…This leads to unreliable results especially in case of false positives. Several approaches for head detection has been proposed previously which are used in different contexts [4,5,6,7,8,9]. [6] proposed human body segmentation approach in order to localize heads in a scene.…”

Section: Introduction and Related Workmentioning

confidence: 99%

FCHD: Fast and accurate head detection in crowded scenes

Vora,

Chilaka

2018

Preprint

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In this paper, we propose FCHD-Fully Convolutional Head Detector, an end-to-end trainable head detection model. Our proposed architecture is a single fully convolutional network which is responsible for both bounding box prediction and classification. This makes our model lightweight with low inference time and memory requirements. Along with run-time, our model has better overall average precision (AP) which is achieved by selection of anchor sizes based on the effective receptive field of the network. This can be concluded from our experiments on several head detection datasets with varying head counts. We achieve an AP of 0.70 on a challenging head detection dataset which is comparable to some standard benchmarks. Along with this our model runs at 5 FPS on Nvidia Quadro M1000M for VGA resolution images. Code is available at https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector.

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