Domain Adaptive Egocentric Person Re-identification

The recent growth of wearable cameras makes the person re-identification (re-ID) from first-person vision data available. Nevertheless, there is still a lack of suitable egocentric vision datasets due to blurriness, illumination change, or poor video quality.

A recent study suggests using a neural style transfer-based domain adaptation technique, which has never been used to bridge the gap between the fixed camera and egocentric datasets.

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The approach uses fixed camera re-ID datasets to improve the performance of egocentric re-ID. Images having features from both egocentric and fixed camera datasets are generated. Then, a pre-trained model is fine-tuned with images from a fixed-camera dataset. The calculated features are then used to re-identify individuals. The use of style-transferred images increased the recognition rate by up to 203.8% compared with non-style transferred images.

Person re-identification (re-ID) in first-person (egocentric) vision is a fairly new and unexplored problem. With the increase of wearable video recording devices, egocentric data becomes readily available, and person re-identification has the potential to benefit greatly from this. However, there is a significant lack of large scale structured egocentric datasets for person re-identification, due to the poor video quality and lack of individuals in most of the recorded content. Although a lot of research has been done in person re-identification based on fixed surveillance cameras, these do not directly benefit egocentric re-ID. Machine learning models trained on the publicly available large scale re-ID datasets cannot be applied to egocentric re-ID due to the dataset bias problem. The proposed algorithm makes use of neural style transfer (NST) that incorporates a variant of Convolutional Neural Network (CNN) to utilize the benefits of both fixed camera vision and first-person vision. NST generates images having features from both egocentric datasets and fixed camera datasets, that are fed through a VGG-16 network trained on a fixed-camera dataset for feature extraction. These extracted features are then used to re-identify individuals. The fixed camera dataset Market-1501 and the first-person dataset EGO Re-ID are applied for this work and the results are on par with the present re-identification models in the egocentric domain.

Research paper: Choudhary, A., Mishra, D., and Karmakar, A., “Domain Adaptive Egocentric Person Re-identification”, 2021. Link: https://arxiv.org/abs/2103.04870

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