Cross-Domain Traffic Scene Understanding by Integrating Deep Learning and Topic Model.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Environmental perception in multicamera surveillance networks is crucial.
• Existing cross-domain methods often fail to utilize local commonalities across dissimilar scenarios.
• Selecting the most similar source domain can lead to performance degradation or negative transfer.

Purpose of the Study:

• To develop a novel framework for cross-domain traffic scene understanding.
• To leverage local commonalities across diverse traffic scenarios for knowledge transfer.
• To avoid the need for source domain selection in cross-domain learning.

Main Methods:

• Integration of deep learning and topic modeling techniques.
• Utilizing labeled activity attribute data from a source domain to annotate a target domain.
• Developing a method that bypasses the requirement of identifying a similar source domain.

Main Results:

• The proposed framework successfully annotates target domain activity attributes without source domain selection.
• Demonstrated effectiveness in transferring knowledge across different traffic scenarios.
• Verified through extensive experiments on public road traffic datasets.

Conclusions:

• The novel framework enhances cross-domain traffic scene understanding by exploiting local commonalities.
• Eliminating the need for source domain selection mitigates performance degradation and negative transfer.
• The approach offers a more robust and adaptable solution for automatic surveillance tasks.