Crowd counting Using DRL-based segmentation and RL-based density estimation
التاريخ
2022المؤلف
Elharrouss, OmarAlmaadeed, Noor
Al-Maadeed, Somaya
Abualsaud, Khalid
Mohamed, Amr
Khattab, Tamer
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البيانات الوصفية
عرض كامل للتسجيلةالملخص
People counting is one of the computer vision tasks that can be useful for crowd management. In addition, estimating the crowdedness of a surveilled scene for crowd behavior analysis is one of the prominent challenges in video surveillance systems. With the introduction of deep learning, this operation has become doable with a convincing performance. However, this task still represents a challenge for these methods. In this regard, we propose a combination of deep reinforcement learning (DRL) networks and deep learning architecture for crowd counting. DRL network used the Context-Aware Attention (CAA) module for segmenting the crowd region, Then, on the segmented results, the crowd density estimation is performed using an encoder-decoder. The proposed method is evaluated and compared with and without the segmentation parts on the existing datasets including UCF-QNRF, UCF-CC-50, ShangaiTech-(A, B), while the obtained results in terms of MAE metric achieved 84,8, 179.2, 44.6, and 8.2 respectively.
المجموعات
- علوم وهندسة الحاسب [2284 items ]