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A practical deep learning face recognition system can be divided into several tasks. These tasks can be time‐consuming if each task is executed with the original image as the input data. And the feature extractors used by different tasks may duplicate its function. In this paper, a multi‐task training method based on feature pyramid and triplet loss to train a single‐stage face detection and face recognition deep neural network is proposed. As a single‐stage work, every task's data is passed through the same backbone network to avoid duplicate computation by sharing the weights and computation. The whole network is established using feature pyramid and anchor boxes to localise the face position, using triplet loss to establish the feature extractor, and finally matching the feature through a simple math function. The benefits of the approach are faster computation speed and less memory usage. On an Nvidia 2080Ti GPU accelerator, this system can achieve 212 FPS for a 640 × 640 resolution input and maintains 92.4% accuracy on the LFW data set. [ABSTRACT FROM AUTHOR]

The capability of recognizing and tracking a specific human is considered a key technique for serving mobile robots. This paper proposes a real‐time tracker that uses a stereo camera to track specific people in different environments. A tracker has been designed to detect the selected target from multiple people by use of block matching algorithm, human detection, and colour histogram comparison. Considering the similar colour of the objects, the predictor of the Kalman filter determines the position of the object. Finally, the mobile robot is moved according to the tracking results with a relative distance between the robot and the target. The effectiveness of the proposed method is demonstrated by experiments on many video sequences and real environments. [ABSTRACT FROM AUTHOR]

Indoor positioning techniques have become very important in recent years. Due to the wide deployment of surveillance cameras, it has become feasible to use the videos for indoor positioning. The success of using this approach can also reduce the load of security persons of watching the monitors all the time. In this study, the authors propose a vision‐based indoor positioning system. The proposed method uses a frame processing technique and applies the Gaussian mixture learning for video background model. The foreground object can be extracted by using the background subtraction. Based on the foreground object, the objects can be tracked and used in the direct linear transform, and generate a bird's‐eye map with camera information. A real‐time demonstration has been also provided. It shows the tracing of the moving objects and the bird's‐eye view. [ABSTRACT FROM AUTHOR]