VikingDet: A Real-time Person and Face Detector for Surveillance Cameras
| Autor: | Zhongxia Xiong, Ma Yalong, Yao Ziying, Xinkai Wu |
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| Rok vydání: | 2019 |
| Předmět: |
Matching (statistics)
Computer science business.industry media_common.quotation_subject 05 social sciences Detector 020207 software engineering 02 engineering and technology Object detection Face (geometry) Data quality 0502 economics and business 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence Set (psychology) business Function (engineering) Face detection 050203 business & management media_common |
| Zdroj: | AVSS |
| Popis: | In this paper, we propose a novel one-stage detector that can simultaneously detect both pedestrians and their faces. The framework is named as VikingDet for its simple but effective two-headed architecture. To tackle the challenges of person and face detection especially under surveillance cameras (e.g. low data quality, complex environments, requirements for efficiency, etc.), we make contributions in the following several aspects: 1) integrating both person and face detection into one network which current leading object detection algorithms are seldomly able to handle; 2) emphasizing detection in low-quality images. we introduce multiple thresholds for matching different sized positive samples, and set proper hyper-parameters, hence our VikingDet is able to locate small objects in surveillance cameras even of low-quality; 3) introducing a training strategy to utilize datasets on hand. Since most available public datasets annotate only people without their faces or faces without bodies, we use multi-step training and an integrated loss function to train VikingDet with these partly annotated data. As a consequence, our detector achieves satisfactory performances in several relative benchmarks with a speed at more than 60 FPS on NVIDIA TITAN X GPU, and can be further deployed on an embedded device such as NVIDIA Jetson TX1 or TX2 with a real-time speed of over 28 FPS. |
| Databáze: | OpenAIRE |
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