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In this paper, we meticulously examine the robustness of computer vision object detection frameworks within the intricate realm of real-world traffic scenarios, with a particular emphasis on challenging adverse weather conditions. Conventional evaluation methods often prove inadequate in addressing the complexities inherent in dynamic traffic environments—an increasingly vital consideration as global advancements in autonomous vehicle technologies persist. Our investigation delves specifically into the nuanced performance of these algorithms amidst adverse weather conditions like fog, rain, snow, sun flare, and more, acknowledging the substantial impact of weather dynamics on their precision. Significantly, we seek to underscore that an object detection framework excelling in clear weather may encounter significant challenges in adverse conditions. Our study incorporates in-depth ablation studies on dual modality architectures, exploring a range of applications including traffic monitoring, vehicle tracking, and object tracking. The ultimate goal is to elevate the safety and efficiency of transportation systems, recognizing the pivotal role of robust computer vision systems in shaping the trajectory of future autonomous and intelligent transportation technologies. 

Big cities are well-known for their traffic congestion and high density of vehicles such as cars, buses, trucks, and even a swarm of motorbikes that overwhelm city streets. Large-scale development projects have exacerbated urban conditions, making traffic congestion more severe. In this paper, we proposed a data-driven city traffic planning simulator. In particular, we make use of the city camera system for traffic analysis. It seeks to recognize the traffic vehicles and traffic flows, with reduced intervention from monitoring staff. Then, we develop a city traffic planning simulator upon the analyzed traffic data. The simulator is used to support metropolitan transportation planning. Our experimental findings address traffic planning challenges and the innovative technical solutions needed to solve them in big cities. 

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 to 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.