Bandwidth Limited Object Recognition in High Resolution Imagery

Abstract

This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance.

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

“…SVD-based characteristics reduction strategy for K-mean clustering algorithm is used to estimate the current condition of flood and flood rating in any place [10] . Convolutional Neural Networks that extract visual features and bidirectional Long Short-Term Memory networks that extract semantic information from textual metadata may predict flood using picture datasets [14] . Multi-parameter analysis Multi-criteria evaluation approaches like Boolean and Weighted Linear Combination can be used to calculate the weights of each component in predicting the flood [11] .…”

Review on Computer Vision Techniques in Emergency Situation

“…To extract relevant information from videos, in [38] the authors find recurrent regions in clips which is normally a sign of relevance. In [103] the authors introduce a framework of consecutive object detection from general to fine that automatically detects important features during an emergency. This also minimizes bandwidth usage, which is essential in many emergency situations where critical communications infrastructure collapses.…”