ABSTRACT
Hand-hygiene is a critical component for safe food handling. In this paper, we apply an iterative engineering process to design a hand-hygiene action detection system to improve food-handling safety. We demonstrate the feasibility of a baseline RGB-only convolutional neural network (CNN) in the restricted case of a single scenario; however, since this baseline system performs poorly across scenarios, we also demonstrate the application of two methods to explore potential reasons for its poor performance. This leads to the development of our hierarchical system that incorporates a variety of modalities (RGB, optical flow, hand masks, and human skeleton joints) for recognizing subsets of hand-hygiene actions. Using hand-washing video recorded from several locations in a commercial kitchen, we demonstrate the effectiveness of our system for detecting hand hygiene actions in untrimmed videos. In addition, we discuss recommendations for designing a computer vision system for a real application.
ABSTRACT
A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refinement classifiers recognize seven specific actions within the hand-hygiene period. We demonstrate that our two-stage system has significantly lower computational requirements without a loss of recognition accuracy. Specifically, the computationally complex refinement classifiers process less than 68% of the untrimmed videos, and we anticipate further computational gains in videos that contain a larger fraction of non-hygiene actions. Our results demonstrate that a carefully designed video action recognition system can play an important role in improving hand hygiene for food safety.
ABSTRACT
Multimedia communication is becoming pervasive because of the progress in wireless communications and multimedia coding. Estimating the quality of the visual content accurately is crucial in providing satisfactory service. State of the art visual quality assessment approaches are effective when the input image and reference image have the same resolution. However, finding the quality of an image that has spatial resolution different than that of the reference image is still a challenging problem. To solve this problem, we develop a quality estimator (QE), which computes the quality of the input image without resampling the reference or the input images. In this paper, we begin by identifying the potential weaknesses of previous approaches used to estimate the quality of experience. Next, we design a QE to estimate the quality of a distorted image with a lower resolution compared with the reference image. We also propose a subjective test environment to explore the success of the proposed algorithm in comparison with other QEs. When the input and test images have different resolutions, the subjective tests demonstrate that in most cases the proposed method works better than other approaches. In addition, the proposed algorithm also performs well when the reference image and the test image have the same resolution.
ABSTRACT
In this paper, we propose a generalized linear model for video packet loss visibility that is applicable to different group-of-picture structures. We develop the model using three subjective experiment data sets that span various encoding standards (H.264 and MPEG-2), group-of-picture structures, and decoder error concealment choices. We consider factors not only within a packet, but also in its vicinity, to account for possible temporal and spatial masking effects. We discover that the factors of scene cuts, camera motion, and reference distance are highly significant to the packet loss visibility. We apply our visibility model to packet prioritization for a video stream; when the network gets congested at an intermediate router, the router is able to decide which packets to drop such that visual quality of the video is minimally impacted. To show the effectiveness of our visibility model and its corresponding packet prioritization method, experiments are done to compare our perceptual-quality-based packet prioritization approach with existing Drop-Tail and Hint-Track-inspired cumulative-MSE-based prioritization methods. The result shows that our prioritization method produces videos of higher perceptual quality for different network conditions and group-of-picture structures. Our model was developed using data from high encoding-rate videos, and designed for high-quality video transported over a mostly reliable network; however, the experiments show the model is applicable to different encoding rates.
ABSTRACT
Little attention has been paid to an impairment common in motion-compensated video compression: the addition of high-frequency (HF) energy as motion compensation displaces blocking artifacts off block boundaries. In this paper, we employ an energy-based approach to measure this motion-compensated edge artifact, using both compressed bitstream information and decoded pixels. We evaluate the performance of our proposed metric, along with several blocking and blurring metrics, on compressed video in two ways. First, ordinal scales are evaluated through a series of expectations that a good quality metric should satisfy: the objective evaluation. Then, the best performing metrics are subjectively evaluated. The same subjective data set is finally used to obtain interval scales to gain more insight. Experimental results show that we accurately estimate the percentage of the added HF energy in compressed video.
