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  1. Retrieving event videos based on textual description is a promising research topic in the fast-growing data field. Since traffic data increases every day, there is an essential need of an intelligent traffic system to speed up the traffic event search. We propose a multi-module system that outputs accurate results. Our solution considers neighboring entities related to the mentioned object to represent an event by rule-based, which can represent an event by the relationship of multiple objects. We also propose to add a modified model from last year's Alibaba model with an explainable architecture. As the traffic data is vehicle-centric, wemore »apply two language and image modules to analyze the input data and obtain the global properties of the context and the internal attributes of the vehicle. We introduce a one-on-one dual training strategy for each representation vector to optimize the interior features for the query. Finally, a refinement module gathers previous results to enhance the final retrieval result. We benchmarked our approach on the data of the AI City Challenge 2022 and obtained the competitive results at an MMR of 0.3611. We were ranked in the top 4 on 50\% of the test set and in the top 5 on the full set.« less
    Free, publicly-accessible full text available June 1, 2023
  2. Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptive label-independent tracking and counting modules that processes 12 frames per second. Our framework adapts hyperparameters for multi-vehicle tracking and properly works in complex traffic conditions, especially invariant to camera perspectives. We achieved the competitive results in terms of root-mean-square error and runtime performance.
    Free, publicly-accessible full text available April 1, 2023
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  7. Sharing real-time data originating from connected devices is crucial to real-world Internet of Things (IoT) applications, especially using artificial intelligence/machine learning (AI/ML). Such IoT data are typically shared with multiple parties for different purposes based on data contracts. However, supporting these contracts under the dynamic change of IoT data variety and velocity faces many challenges when such parties (aka tenants) want to obtain data based on the data value to their specific contextual purposes. This work proposes a novel dynamic context-based policy enforcement framework to support IoT data sharing based on dynamic contracts. Our enforcement framework allows IoT Data Hubmore »owners to define extensible rules and metrics to govern the tenants in accessing the shared data on the Edge based on policies defined in static and dynamic contexts. For example, given the change of situations, we can define and enforce a policy that allows pushing data to some tenants via a third-party means, while typically, these tenants must obtain and process the data based on a pre-defined means. We have developed a proof-of-concept prototype for sharing sensitive data such as surveillance camera videos to illustrate our proposed framework. Our experimental results demonstrated that our framework could soundly and timely enforce context-based policies at runtime with moderate overhead. Moreover, the context and policy changes are correctly reflected in the system in nearly real-time.« less
    Free, publicly-accessible full text available January 1, 2023
  8. Free, publicly-accessible full text available October 1, 2022
  9. Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight tomore »yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.« less
    Free, publicly-accessible full text available October 1, 2022
  10. Traffic event retrieval is one of the important tasks for intelligent traffic system management. To find accurate candidate events in traffic videos corresponding to a specific text query, it is necessary to understand the text query's attributes, represent the visual and motion attributes of vehicles in videos, and measure the similarity between them. Thus we propose a promising method for vehicle event retrieval from a natural-language-based specification. We utilize both appearance and motion attributes of a vehicle and adapt the COOT model to evaluate the semantic relationship between a query and a video track. Experiments with the test dataset ofmore »Track 5 in AI City Challenge 2021 show that our method is among the top 6 with a score of 0.1560.« less