Footprint is a human biological trait with a high extraction rate from crime scenes. It plays an essential role in criminal investigation and security operations. The majority of footprint studies are currently conducted under controlled conditions. However, footprints formed under natural walking are affected by physiological and behavioral characteristics such as posture and walking state, and have variability, which makes it difficult to accurately identify footprints. In this paper, multi-state barefoot pressure images under natural walking state are taken as the research object, and the selective attention network is used for high precision recognition. It can provide a theoretical foundation as well as technical support for later comparison and identification of footprints on-site.
To evaluate the rehabilitation effect of elderly people during walking training on treadmills, a detection system capable of acquiring gait features was designed. Install the flexible force-sensitive sensor on the treadmill table, collect sensor data through STM32F205 microcontroller, and use W5300 Ethernet chip and STM32F205 microcontroller to build a TCP/IP network communication platform to ensure that sensor data can be synchronously transmitted to the host computer through the network port in real-time. The gait characteristics of the elderly are obtained by calculating the sensor data obtained by the host computer. System tests show that the gait characteristics obtained when walking on a treadmill are consistent with previous research results, thus verifying the accuracy of the detection system.
Ski jumping is a fast and wide-range motion. Although wearable devices are available to analyze the motion, it is cumbersome and difficult to implement. Since video data is relatively simple to obtain, this paper proposes a video-based method for estimating the pose of ski jumpers. In this method, we use a high-speed camera as a video data collector, and use Simi Motion software to convert the video into frames and manually annotate keypoints. The video data of three athletes is used to build the training set, and another is used to build the test set. In addition, we use High-Resolution Net (HRNet) to transfer the learning of feature knowledge from the public dataset COCO2017 to the task of ski jumpers pose estimation. The experiments show that under the real labeled bounding box, an average precision of 84.6% is obtained, which is higher than other mainstream human pose estimate methods.
Footprints are important information at the crime scene and play an important role in the field of criminal investigation. At present, the research on footprints mainly focuses on barefoot footprints, but the main thing obtained at the crime scene is shoeprints. How to mine barefoot footprints through shoeprints is one point of the key problems in the field of footprint recognition. This paper takes optical footprints as the research object, collects 95 people’s cloth shoeprints and barefoot footprints, and proposes a generative adversarial network which combines self-attention modules and multiscale discriminator (SM-GAN). The self-attention module is added to the generator, which enables the network to focus on the association between footprint structures. The discriminator uses a multiscale discriminator structure, which improves the generation effect of the generated image in the global and local areas. The experimental results show that the method proposed in this paper has a better effect of generating footprints than traditional image-to-image translation methods.
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