Improving acoustic vehicle classification by information fusion

We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac

Synthetic Data-Enhanced Deep Learning For Quality Control Of Automated Welding Processes

Automotive production systems are designed to produce large quantities in high quality and short throughput times and are therefore organized as line production. This places high quality requirements on the joining processes in automotive body shops, in which automated, robot-guided welding is a key process. The quality of these thermal joining processes depends on various physical and chemical influencing factors, whose interactions cannot be explicitly modelled. This leads to enormous quality assurance efforts in several quality control loops, which may include visual inspections, non-destructive testing of samples to assess the internal structure and destructive testing of samples for the assessment of mechanical properties such as tensile strength. Due to the increasing availability of data in automated processes and the complexity of welding processes, the application of Deep Learning has a great potential to reduce quality control efforts in automotive body shops. Using Deep Learning to leverage process data and accurately predict quality parameters in welding processes is investigated in research, yet model training requires a large, balanced and annotated dataset, whose generation is time and cost intensive, particularly for production data. However, there are generative AI methods such as Generative Adversarial Networks (GANs) that are able to generate synthetic data and thus offer the potential to generate a large amount of annotated production data with relatively little effort. This paper presents a systematic approach to evaluate the potential of incorporating synthetic data in a real-world production dataset to improve quality control using Deep Learning. The approach is validated for the analysis of real-world ultrasound images of resistance spot welding (RSW) processes from the automotive industry. Different Deep Learning architectures to generate synthetic data are compared. Results show that adding synthetic data to the training dataset can improve the accuracy of Deep Learning models for quality monitoring in welding processes

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations

Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos

We propose a new zero-shot Event Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available modalities from videos into a distributional semantic space. To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) automatically determining relevance of concepts/attributes to a free text query, which could be useful for other applications, and (c) retrieving videos by free text event query (e.g., "changing a vehicle tire") based on their content. We embed videos into a distributional semantic space and then measure the similarity between videos and the event query in a free text form. We validated our method on the large TRECVID MED (Multimedia Event Detection) challenge. Using only the event title as a query, our method outperformed the state-of-the-art that uses big descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC metric. It is also an order of magnitude faster.Comment: To appear in AAAI 201

TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories

Robustly classifying ground infrastructure such as roads and street crossings is an essential task for mobile robots operating alongside pedestrians. While many semantic segmentation datasets are available for autonomous vehicles, models trained on such datasets exhibit a large domain gap when deployed on robots operating in pedestrian spaces. Manually annotating images recorded from pedestrian viewpoints is both expensive and time-consuming. To overcome this challenge, we propose TrackletMapper, a framework for annotating ground surface types such as sidewalks, roads, and street crossings from object tracklets without requiring human-annotated data. To this end, we project the robot ego-trajectory and the paths of other traffic participants into the ego-view camera images, creating sparse semantic annotations for multiple types of ground surfaces from which a ground segmentation model can be trained. We further show that the model can be self-distilled for additional performance benefits by aggregating a ground surface map and projecting it into the camera images, creating a denser set of training annotations compared to the sparse tracklet annotations. We qualitatively and quantitatively attest our findings on a novel large-scale dataset for mobile robots operating in pedestrian areas. Code and dataset will be made available at http://trackletmapper.cs.uni-freiburg.de.Comment: 19 pages, 14 figures, CoRL 2022 v

Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems