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Found 7 papers, 0 papers with code
Real-time Driver Monitoring Systems on Edge AI Device
As road accident cases are increasing due to the inattention of the driver, automated driver monitoring systems (DMS) have gained an increase in acceptance.
Understanding the impact of mistakes on background regions in crowd counting
Our results show that (i) mistakes on background are substantial and they are responsible for 18-49% of the total error, (ii) models do not generalize well to different kinds of backgrounds and perform poorly on completely background images, and (iii) models make many more mistakes than those captured by the standard Mean Absolute Error (MAE) metric, as counting on background compensates considerably for misses on foreground.
Multi-Scale Attention Network for Crowd Counting
In crowd counting datasets, people appear at different scales, depending on their distance from the camera.
Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification
However, current networks extract fixed representations for each image regardless of other images which are paired with it and the comparison with other images is done only at the final level.
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Person Re-Identification
on Market-1501
A Siamese Long Short-Term Memory Architecture for Human Re-Identification
Matching pedestrians across multiple camera views known as human re-identification (re-identification) is a challenging problem in visual surveillance.
Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification
This paper addresses the problem of matching pedestrians across multiple camera views, known as person re-identification.
Learning Invariant Color Features for Person Re-Identification
We model color feature generation as a learning problem by jointly learning a linear transformation and a dictionary to encode pixel values.
