Real-time Pedestrian Attribute Detection from Surveillance Cameras

In recent years, computer vision has taken great strides in understanding and recognition the visual scenes together with deep learning technologies. Object recognition is one of the key areas of the computer vision applications. It is mainly concerned with recognition and localization of specific objects in an image. There are open-source and pre-trained models for the detection of general objects (such as cars, persons, cats, dogs). However, it is necessary to develop problem-based algorithms and training with deep neural networks by creating annotated training data for the recognition of special objects (such as headscarf). Furthermore, objects come in different shapes, sizes, angles, colors and additional noise from changes in the realworld environment, perspective, lighting and shadows. Taking into account these problems and needs in the real world, this paper focuses on the development of problem-based deep neural networks algorithms and the creation of labeled and reliable training datasets for the objects to be recognized. The contribution of the paper is to make use of transfer learning with the optimized R-FCN and Faster R-CNN pre-trained models in order to recognize pedestrian attributes including hat, headscarf, eyeglasses, bag objects and gender from security cameras. The proposed detection model has been trained on large-scale labeled dataset using TensorFlow open source platform. The performance of the neural network model has been evaluated using Average Precision (AP) values for each class and over 75% Mean Average Precision (mAP) for all classes is achieved.