Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance
作者：
Highlights：
• We propose and evaluate a two level methodology called ODeBiC, based on the use of deep learning, to improve the detection of small objects that can be handled similarly. The first level uses a detector to select from each input frame the candidate regions with a specific confidence about the presence of each object. Then, the second level analyses these proposals using a binarization technique to identify the objects with higher accuracy. ODeBiC methodology maintains a good accuracy for the detection of large objects as well.
• We analyse the potential of binarization techniques such as, OVA and OVO, to improve the detection of small objects, manipulated with hand, that can be confused with a weapon. As far as we know, this is the first study in analysing such potential.
• We build a new dataset called Sohas_weapon (small objects handled similarly to a weapon, dataset) for the case study of six small objects that are often handled in a similar way to a weapon: pistol, knife, smartphone, bill, purse and card. We used different camera and surveillance camera technologies to take the images. 10% of the images were downloaded from Internet. All these images were manually annotated for the detection task. This useful dataset will be available for other studies (http://sci2s.ugr.es/weapons-detection).
摘要
•We propose and evaluate a two level methodology called ODeBiC, based on the use of deep learning, to improve the detection of small objects that can be handled similarly. The first level uses a detector to select from each input frame the candidate regions with a specific confidence about the presence of each object. Then, the second level analyses these proposals using a binarization technique to identify the objects with higher accuracy. ODeBiC methodology maintains a good accuracy for the detection of large objects as well.•We analyse the potential of binarization techniques such as, OVA and OVO, to improve the detection of small objects, manipulated with hand, that can be confused with a weapon. As far as we know, this is the first study in analysing such potential.•We build a new dataset called Sohas_weapon (small objects handled similarly to a weapon, dataset) for the case study of six small objects that are often handled in a similar way to a weapon: pistol, knife, smartphone, bill, purse and card. We used different camera and surveillance camera technologies to take the images. 10% of the images were downloaded from Internet. All these images were manually annotated for the detection task. This useful dataset will be available for other studies (http://sci2s.ugr.es/weapons-detection).
论文关键词：Detection,Convolutional neuronal networks,One-Versus-All,One-Versus-One
论文评审过程：Received 28 October 2019, Revised 28 January 2020, Accepted 30 January 2020, Available online 4 February 2020, Version of Record 18 May 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.105590
