Abstract
Vehicle searching from videos by textual descriptions is one of the most important tasks in traffic management towards smart cities. This paper proposes a method for retrieval of vehicles using a natural language-based query. Our method consists of two main components of textual extractor based on Bi-LSTM and visual extractor using ResNet-50 model. Both components extract hidden features from different modalities and then match them in a common space. This end-to-end process tries to build a textual-visual alignment model that will be utilized for the search phase. Our particularities in this framework are two-fold. In the video stream, we evaluate in detail the role of vehicle appearance compared to its motion. In the textual stream, we apply back-translation systems to enrich the textual dataset for the training phase. Experiments are conducted on AI City Challenging, showing the efficiency of each contribution in the overall framework. It confirms that not only appearance but additional motion cues are promising for vehicle retrieval, which provides the results of MRR, Rank@5 and Rank@10 are 0.2333, 0.3587 and 0.4837, respectively.
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This research is funded by the Vietnam MPS under grant number BCN. 2020. T01. 04.
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Can, QH. et al. (2022). Exploring the Effect of Vehicle Appearance and Motion for Natural Language-Based Vehicle Retrieval. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_5
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