The emerging machine learning methods and models require large and well-annotated benchmark datasets in order to increase their generalization capacity. Pedestrian detection is one of the major research fields that suffer from data set dependency and over-fitting. A significant portion of the ongoing studies uses transfer learning or similar adaptation strategies based on deep models, which are initially trained on a benchmark dataset. This paper searches for an answer. Unfortunately, the selection of an adequate model is usually based on trial and error, which requires a tedious and time-consuming simulation through potential candidates. This paper proposes a computationally simple analysis strategy for selecting a good candidate using Shannon Entropy and Gray Level Co-occurrence Matrices, which are utilized to determine the similarity and usability of a pre-trained data set on an independent test dataset. For this purpose, a new dataset is also presented together with a detailed analysis of its characteristics. The results show that the similarity of entropy distribution of annotations (i.e. bounding boxes) has the most important characteristic to achieve robust adaptation after training,
Selecting Adequate Basis Model for Adaptation in Pedestrian Detection
2022-10-08
670155 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
PEDESTRIAN DETECTION DEVICE, PEDESTRIAN DETECTION SYSTEM, AND PEDESTRIAN DETECTION METHOD
Europäisches Patentamt | 2018
|Pedestrian delay at fixed time pelican crossings - is a single vehicle precedence period adequate?
British Library Conference Proceedings | 1992
|