Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over a 31.10% ROC score.Comment: Accepted by IEEE ISCAS 2022, 5 pages, 2 figures. arXiv admin note: text overlap with arXiv:2110.0336

Unified Chinese License Plate Detection and Recognition with High Efficiency

Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP images as a supplement to the existing public benchmarks. The images are mainly captured with electronic monitoring systems with detailed annotations. To our knowledge, CRPD is the largest public multi-objective Chinese LP dataset with annotations of vertices. With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline. The network is end-to-end trainable with totally real-time inference efficiency (30 fps with 640p). The experiments on several public benchmarks demonstrate that our method has reached competitive performance. The code and dataset will be publicly available at https://github.com/yxgong0/CRPD

TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China

TiEV is an autonomous driving platform implemented by Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously in urban paths as well as on fast express roads. We describe our whole system, especially novel modules of probabilistic perception fusion, incremental mapping, the 1st and the 2nd planning and the overall safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future Challenge of China held at Changshu. We show our experiences on the development of autonomous vehicles and future trends

Tunnel Magnetoresistance Sensor with AC Modulation and Impedance Compensation for Ultra-Weak Magnetic Field Measurement

Tunnel magnetoresistance (TMR) is a kind of magnetic sensor with the advantages of low cost and high sensitivity. For ultra-weak and low-frequency magnetic field measurement, the TMR sensor is affected by the 1/f noise. This paper proposes an AC modulation method with impedance compensation to improve the performance. The DC and AC characteristics of the sensors were measured and are presented here. It was found that both the equivalent resistance and capacitor of the sensors are affected by the external magnetic field. The TMR sensors are connected as a push–pull bridge circuit to measure the magnetic field. To reduce the common-mode noise, two similar bridge circuits form a magnetic gradiometer. Experimental results show that the sensor’s sensitivity in the low-frequency range is obviously improved by the modulation and impedance compensation. The signal-to-noise ratio of the sensor at 1 Hz was increased about 25.3 dB by the AC modulation, impedance compensation, and gradiometer measurement setup. In addition, the sensitivity of the sensor was improved from 165.2 to 222.1 mV/V/mT. Ultra-weak magnetic signals, namely magnetocardiography signals of two human bodies, were measured by the sensor in an unshielded environment. It was seen that the R peak of MCG can be clearly visualized from the recorded signal