Pedestrian Detection by Scene Adaptation Based on False Positive Mining
نویسندگان
چکیده
منابع مشابه
Scene-Specific Pedestrian Detection Based on Parallel Vision
As a special type of object detection, pedestrian detection in generic scenes has made a significant progress trained with large amounts of labeled training data manually. While the models trained with generic dataset work bad when they are directly used in specific scenes. With special viewpoints, flow light and backgrounds, datasets from specific scenes are much different from the datasets fr...
متن کاملDomain Adaptation for Pedestrian Detection Based on Prediction Consistency
Pedestrian detection is an active area of research in computer vision. It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene. In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples ...
متن کاملScanner Detection Based on Connection Attempt Success Ratio with Guaranteed False Positive and False Negative Probabilities
Since the link rate is very high up to 40Gbps these days, scanning packets can spread very fast. At this high speed, only a small chance of missing on-going scanning activity can lead to catastrophic results. Thus, fast and accurate detection of scanners is a very important problem. High-speed packet processing usually requires high-speed memory, SRAM, and the size of SRAM is very limited compa...
متن کاملUnsupervised Deep Domain Adaptation for Pedestrian Detection
This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes. First, we utilize an iterative algorithm to iteratively select and auto-annotate positive pedestrian samples with high confidence as the training samples for the target domain. Meanwhile, we also reuse negative samples from the source domain to compensate for the imbalance b...
متن کاملDeep Learning of Scene-Specific Classifier for Pedestrian Detection
The performance of a detector depends much on its training dataset and drops significantly when the detector is applied to a new scene due to the large variations between the source training dataset and the target scene. In order to bridge this appearance gap, we propose a deep model to automatically learn scene-specific features and visual patterns in static video surveillance without any manu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Japan Society for Precision Engineering
سال: 2016
ISSN: 0912-0289,1882-675X
DOI: 10.2493/jjspe.82.1085