联想(心理学)
计算机科学
数据关联
人工智能
目标检测
计算机视觉
跟踪(教育)
对象(语法)
视频跟踪
模式识别(心理学)
心理学
教育学
概率逻辑
心理治疗师
作者
Ajit Jadhav,Prerana Mukherjee,Vinay Kaushik,Brejesh Lall
出处
期刊:National Conference on Communications
日期:2020-02-01
被引量:10
标识
DOI:10.1109/ncc48643.2020.9056035
摘要
A lot a research is focused on object detection and it has achieved significant advances with deep learning techniques in recent years. Inspite of the existing research, these algorithms are not usually optimal for dealing with sequences or images captured by drone-based platforms, due to various challenges such as view point change, scales, density of object distribution and occlusion. In this paper, we develop a model for detection of objects in drone images using the VisDrone2019 DET dataset. Using the RetinaNet model as our base, we modify the anchor scales to better handle the detection of dense distribution and small size of the objects. We explicitly model the channel interdependencies by using “Squeeze-and-Excitation” (SE) blocks that adaptively recalibrates channel-wise feature responses. This helps to bring significant improvements in performance at a slight additional computational cost. Using this architecture for object detection, we build a custom DeepSORT network for object detection on the VisDrone2019 MOT dataset by training a custom Deep Association network for the algorithm.
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