TY - GEN
T1 - Enhanced Vehicle Re-identification for ITS
T2 - 2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022
AU - Ashutosh Holla, B.
AU - Manohara Pai, M. M.
AU - Verma, Ujjwal
AU - Pai, Radhika M.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic problems. As an application of ITS, vehicle re-identification has gained ample interest in the domain of computer vision and robotics. Convolutional neural network (CNN) based methods are developed to perform vehicle re-identification to address key challenges such as occlusion, illumination change, scale, etc. The advancement of transformers in computer vision has opened an opportunity to explore the re-identification process further to enhance performance. In this paper, a framework is developed to perform the re-identification of vehicles across CCTV cameras. To perform re-identification, the proposed framework fuses the vehicle representation learned using a CNN and a transformer model. The framework is tested on a dataset that contains 81 unique vehicle identities observed across 20 CCTV cameras. From the experiments, the fused vehicle re-identification framework yields an mAP of 61.73% which is significantly better when compared with the standalone CNN or transformer model.
AB - In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic problems. As an application of ITS, vehicle re-identification has gained ample interest in the domain of computer vision and robotics. Convolutional neural network (CNN) based methods are developed to perform vehicle re-identification to address key challenges such as occlusion, illumination change, scale, etc. The advancement of transformers in computer vision has opened an opportunity to explore the re-identification process further to enhance performance. In this paper, a framework is developed to perform the re-identification of vehicles across CCTV cameras. To perform re-identification, the proposed framework fuses the vehicle representation learned using a CNN and a transformer model. The framework is tested on a dataset that contains 81 unique vehicle identities observed across 20 CCTV cameras. From the experiments, the fused vehicle re-identification framework yields an mAP of 61.73% which is significantly better when compared with the standalone CNN or transformer model.
UR - http://www.scopus.com/inward/record.url?scp=85138253092&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138253092&partnerID=8YFLogxK
U2 - 10.1109/CONECCT55679.2022.9865740
DO - 10.1109/CONECCT55679.2022.9865740
M3 - Conference contribution
AN - SCOPUS:85138253092
T3 - 2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022
BT - 2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 July 2022 through 10 July 2022
ER -