Identification of poor visibility conditions in urban settings

Gudwani, Hitesh; Singh, Vinit; Mahajan, Shveta; Mittal, Deepti; Das, Amitava

doi:10.1109/icccnt.2017.8204116

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…In this study, the focus is on the face detection-based person re-identification method. The other challenges in implementing person re-identification using face detection from videos are occlusion (Zhuo et al, 2018), poor quality of the video, (Hitesh et al, 2017), partial faces (Liao et al, 2012), etc. The nature of the input dataset is always a factor that affects the performance of any model (Mathew et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Face Verification for Person Re-Identification from Surveillance Camera and Drone-based Videos

Mathew¹,

Ramesh²,

Toby³

et al. 2021

Journal of Computer Science

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Person re-identification in surveillance camera videos is attracting widespread interest due to its increasing number of applications. It is being applied in the field of security, healthcare, product manufacturing, product sales and more. Though there are a variety of methods to do person reidentification, face verification-based methods are very much effective. In this study, a deep learning framework to perform face verification in videos is proposed. Face verification deep learning model development includes different stages like face recognition, cropping, alignment, augmentation, image enhancement and face image selection for model training. The authors have put forward innovative methods to be adopted in various stages of this sequence to improve the performance of the models. The focus of this study is on these image preprocessing stages of the process, rather than the deep learning part, which makes the approach unique. The overall model is improvised by increasing the efficiency of each of these stages by adopting methods like face recognition and cropping based on face landmarks, effective training image selection using face landmark symmetry, various image augmentation techniques including perspective transformation and image enhancement methods like contrast stretching and histogram equalization. An average two percent increase is obtained in the accuracy of the face verification models by applying these methods.

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Section: Literature Reviewmentioning

confidence: 99%

Face Verification for Person Re-Identification from Surveillance Camera and Drone-based Videos

Mathew¹,

Ramesh²,

Toby³

et al. 2021

Journal of Computer Science

View full text Add to dashboard Cite

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“…Its performance can decrease under such conditions, as the visual sensors may not capture enough detail to accurately differentiate between various elements in the scene. This sensitivity could be a hindrance in operations conducted during nighttime or in areas with poor visibility [64][65][66]. Lastly, the model's ability to detect small obstacles is an area of concern, especially in safety-critical applications.…”

mentioning

confidence: 99%

Urban Aquatic Scene Expansion for Semantic Segmentation in Cityscapes

Yue,

Lo,

et al. 2024

Urban Science

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In urban environments, semantic segmentation using computer vision plays a pivotal role in understanding and interpreting the diverse elements within urban imagery. The Cityscapes dataset, widely used for semantic segmentation in urban scenes, predominantly features urban elements like buildings and vehicles but lacks aquatic elements. Recognizing this limitation, our study introduces a method to enhance the Cityscapes dataset by incorporating aquatic classes, crucial for a comprehensive understanding of coastal urban environments. To achieve this, we employ a dual-model approach using two advanced neural networks. The first network is trained on the standard Cityscapes dataset, while the second focuses on aquatic scenes. We adeptly integrate aquatic features from the marine-focused model into the Cityscapes imagery. This integration is carefully executed to ensure a seamless blend of urban and aquatic elements, thereby creating an enriched dataset that reflects the realities of coastal cities more accurately. Our method is evaluated by comparing the enhanced Cityscapes model with the original on a set of diverse urban images, including aquatic views. The results demonstrate that our approach effectively maintains the high segmentation accuracy of the original Cityscapes dataset for urban elements while successfully integrating marine features. Importantly, this is achieved without necessitating additional training, which is a significant advantage in terms of resource efficiency.

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Identification of poor visibility conditions in urban settings

Cited by 2 publications

References 6 publications

Face Verification for Person Re-Identification from Surveillance Camera and Drone-based Videos

Face Verification for Person Re-Identification from Surveillance Camera and Drone-based Videos

Urban Aquatic Scene Expansion for Semantic Segmentation in Cityscapes

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