A vision-based framework for human behavior understanding in industrial assembly lines

This paper introduces a vision-based framework for capturing and understanding human behavior in industrial assembly lines, focusing on car door manufacturing. The framework leverages advanced computer vision techniques to estimate workers' locations and 3D poses and analyze work postures, actions, and task progress. A key contribution is the introduction of the CarDA dataset, which contains domain-relevant assembly actions captured in a realistic setting to support the analysis of the framework for human pose and action analysis. The dataset comprises time-synchronized multi-camera RGB-D videos, motion capture data recorded in a real car manufacturing environment, and annotations for EAWS-based ergonomic risk scores and assembly activities. Experimental results demonstrate the effectiveness of the proposed approach in classifying worker postures and robust performance in monitoring assembly task progress.

COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images

We present a generic algorithm for scoring pose estimation methods that rely on single image semantic analysis. The algorithm employs a lightweight putative shape representation using a combination of multiple Gaussian Processes. Each Gaussian Process (GP) yields distance normal distributions from multiple reference points in the object's coordinate system to its surface, thus providing a geometric evaluation framework for scoring predicted poses. Our confidence measure comprises the average mixture probability of pixel back-projections onto the shape template. In the reported experiments, we compare the accuracy of our GP based representation of objects versus the actual geometric models and demonstrate the ability of our method to capture the influence of outliers as opposed to the corresponding intrinsic measures that ship with the segmentation and pose estimation methods.

The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes

The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the usual ComPaRE and BoAW features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectRum toolkit; in addition, we add end-to-end sequential modelling, and a log-mel-128-BNN.