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Indiana University-Purdue University Indianapolis (IUPUI) / Action recognition has been an active research topic for over three decades. There are various applications of action recognition, such as surveillance, human-computer interaction, and content-based retrieval. Recently, research focuses on movies, web videos, and TV shows datasets. The nature of these datasets make action recognition very challenging due to scene variability and complexity, namely background clutter, occlusions, viewpoint changes, fast irregular motion, and large spatio-temporal search space (articulation configurations and motions). The use of local space and time image features shows promising results, avoiding the cumbersome and often inaccurate frame-by-frame segmentation (boundary estimation). We focus on two state of the art methods for the action classification problem: dense trajectories and recurrent neural networks (RNN). Dense trajectories use typical supervised training (e.g., with Support Vector Machines) of features such as 3D-SIFT, extended SURF, HOG3D, and local trinary patterns; the main idea is to densely sample these features in each frame and track them in the sequence based on optical flow. On the other hand, the deep neural network uses the input frames to detect action and produce part proposals, i.e., estimate information on body parts (shapes and locations). We compare qualitatively and numerically these two approaches, indicative to what is used today, and describe our conclusions with respect to accuracy and efficiency.

Indiana University-Purdue University Indianapolis (IUPUI) / Humans can visually see things and can differentiate objects easily but for computers, it is not that easy. Computer Vision is an interdisciplinary field that allows computers to comprehend, from digital videos and images, and differentiate objects. With the Introduction to CNNs/DNNs, computer vision is tremendously used in applications like ADAS, robotics and autonomous systems, etc. This thesis aims to propose an architecture, RMNv2, that is well suited for computer vision applications such as ADAS, etc. RMNv2 is inspired by its original architecture Mobilenet V2. It is a modified version of Mobilenet V2. It includes changes like disabling downsample layers, Heterogeneous kernel-based convolutions, mish activation, and auto augmentation. The proposed model is trained from scratch in the CIFAR10 dataset and produced an accuracy of 92.4% with a total number of parameters of 1.06M. The results indicate that the proposed model has a model size of 4.3MB which is like a 52.2% decrease from its original implementation. Due to its less size and competitive accuracy the proposed model can be easily deployed in resource-constrained devices like mobile and embedded devices for applications like ADAS etc. Further, the proposed model is also implemented in real-time embedded devices like NXP Bluebox 2.0 and NXP i.MX RT1060 for image classification tasks.

In recent years, various high-performance networks have been proposed for single-image object detection. An obvious choice is to design a video detection network based on state-of-the-art single-image detectors. However, video object detection is still challenging due to the lower quality of individual frames in a video, and hence the need to include temporal information for high-quality detection results. In this thesis, we design a novel interleaved architecture combining a 2D convolutional network and a 3D temporal network. We utilize Yolov3 as the base detector. To explore inter-frame information, we propose feature aggregation based on a temporal network. Our temporal network utilizes Appearance-preserving 3D convolution (AP3D) for extracting aligned features in the temporal dimension. Our multi-scale detector and multi-scale temporal network communicate at each scale and also across scales. The number of inputs of our temporal network can be either 4, 8, or 16 frames in this thesis and correspondingly we name our temporal network TemporalNet-4, TemporalNet-8 and TemporalNet-16. Our approach achieves 77.1\% mAP (mean Average Precision) on ImageNet VID 2017 dataset with TemporalNet-4, where TemporalNet-16 achieves 80.9\% mAP which is a competitive result on this video object detection benchmark. Our network is also real-time with a running time of 35ms/frame.

This essay aims to ascertain whether an impulse response is able to accurately simulatethe sound of an amplifier with a clean tone. This is done with a visual analysis of thewaveforms from both a re-amped audio signal and one with a convolution reverb/loaderwith an impulse measurement of the amplifier. And an audio evaluation with musicallyknowledgeable individuals. The results show small visual differences on the waveformsand no participant in the audio evaluation test was able to tell which was an impulseresponse. Leading to the conclusion that an impulse response could simulate theamplifier with great accuracy.

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According to various studies, compared with novice athletes, experts exhibit superior integration of perceptual, cognitive, and motor skills. This superior ability has been associated with the focused and efficient organization of task-related neural networks. Specifically, skilled individuals demonstrate a spatially localized or relatively lower response in brain activity, characterized as ‘neural efficiency’, when performing within their domain of expertise. Previous works also suggested that elite basketball players can predict successful free throws more rapidly and accurately based on cues from body kinematics. These traits are the result of a prolonged training of specific motor skills and focused excitability of the motor cortex during the reaction, movement planning, and execution phases. However, due to motion artifacts occurring during movement initiation and execution, the knowledge about the underlying mechanism and the connection between brain neural networks and body musculoskeletal systems is still limited. Thus, the objective of this study is to utilize electroencephalography (EEG) and motion capture systems (MoCap) to advance and expand the current understanding of the relationships between neurophysiological activities and human biomechanics as well as their effects on the success rate of the motor skills. The project focuses on fulfilling three specific aims. The first aim focused on the integration of the EEG and the MoCap systems to analyze and compare the successful and unsuccessful outcomes of basketball throws. Then, the second aim utilized Convolution Neural Networks (CNNs) as an alternative approach to predict the shot’s outcome based on the recorded EEG signals and biomechanical parameters. Lastly, the third aim identified the impact of each EEG channel and MoCap parameter on the CNN models using ablation methods. The results obtained from this study can be a practical approach in analyzing the sources that lead to better elite athletes’ performance in various sport-related tasks. Moreover, the acquired data can contribute to a deeper understanding of the vital biomechanical and neurological factors that directly affect the performance of elite athletes during successful outcomes, thus, providing vital information for the overall improvement of athletic performance and guidance for sport-specific training needs.

Managing crowd density has become an immense challenge for public authorities due to population growth and evolving human dynamics. Crowd counting estimates the number of individuals in a given area or scene, making it a practical technique applicable in real-world scenarios such as surveillance and traffic control. It contributes to urban planning, retail analytics, and security systems by providing insights into population dynamics and aiding in anomaly detection. This thesis focuses on implementing and evaluating a selective kernel mechanism in crowd counting. The selective kernel block, introduced in a computer vision research known as the Selective Kernel (SK) Network [1], presents an adapted convolution layer as a substitute for the traditional convolution neural network (CNN) architecture. This adaptation has the potential to enhance object detection and image regression tasks. Building upon the C3 framework [2], the thesis applies the selective kernel mechanism to three state-of-the-art crowd counting designs: ResNet [3], CSRNet [4], and SANet [5], resulting in the creation of SK adaptive models. The evaluation process mainly involves collecting and comparing Mean Absolute Error (MAE) and Mean Squared Error (MSE), as well as crowd statistics and crowd density maps. These evaluations are performed using the ShanghaiTech crowd Part A (random high-density crowd images from the website) and Part B (street views in similar scenes) datasets [6]. In 6 comparisons with two different datasets, SK adaptive models were found to have better prediction results in 4 of them against the original models. In conclusion, the SK block offers several advantages: firstly, it enhances feature extraction performance, especially when pretrained with large datasets; secondly, it improves image regression in more straightforward dataset scenarios. On the downside, its impact is limited or detrimental in sparse datasets. This finding suggests that the selective kernel approach holds promise in supporting and improving crowd counting in the high-density group and street view scenarios, facilitating effective public management. / Att hantera folktäthet har blivit en enorm utmaning för offentliga myndigheter på grund av befolkningsökning och förändrade mänskliga dynamiker. Folkräkning uppskattar antalet individer i ett givet område eller scen, vilket gör det till en praktisk teknik som kan tillämpas i verkliga scenarier som övervakning och trafikstyrning. Genom att erbjuda insikter i befolkningsdynamik och hjälpa till med avvikelsedetektering bidrar folkräkning till stadsplanering, detaljhandelsanalys och säkerhetssystem. Denna avhandling fokuserar på implementeringen och utvärderingen av den selektiva kernelmekanismen inom folksamlingars räkning. Den selektiva kernelblocket, introducerat i en datorseendeforskning känd som Selective Kernel Network [1], presenterar en anpassad faltningsskikt som en ersättning för den traditionella konvolutionsneuralnätverk-arkitekturen. Denna anpassning har potential att förbättra objektdetektion och bildregression. Byggande på C3 - ramverket [2] tillämpar avhandlingen den selektiva kernelmekanismen på tre toppmoderna modeller inom folksamlingars räkning: ResNet [3], CSRNet [4], och SANet [5], vilket resulterar i skapandet av SK-adaptiva modeller. Evalueringen innefattar främst insamling och jämförelse av medelabsolutfel och medelkvadratfel, samt statistik om folksamlingar och densitetskartor. Dessa utvärderingar utförs med hjälp av dataseten ShanghaiTech crowd Part A (slumpmässiga bilder av hög densitet från webbplatsen) och Part B (gatuvyer i liknande scenarier) [6]. Totalt genomförs sex jämförelser med två olika dataset, och SK-adaptiva modeller visar bättre prognosresultat i fyra av dem jämfört med de ursprungliga modellerna. Sammanfattningsvis erbjuder SK-blocket flera fördelar: för det första förbättrar det prestandan för funktionsextrahering, särskilt när det förtränas med stora dataset; för det andra förbättrar det bildregression i enklare dataset-scenarier. Å andra sidan är dess påverkan begränsad eller till och med skadlig i glesa dataset. Generellt sett tyder detta på att den selektiva kärnan har lovande att stödja och förbättra publikräkningen i scenarierna med hög täthet och gatuvy, och därigenom underlätta effektiv offentlig förvaltning.

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