Adapting Computer Vision Models To Limitations On Input Dimensionality And Model Complexity

When considering instances of distributed systems where visual sensors communicate with remote predictive models, data traffic is limited to the capacity of communication channels, and hardware limits the processing of collected data prior to transmission. We study novel methods of adapting visual inference to limitations on complexity and data availability at test time, wherever the aforementioned limitations exist. Our contributions detailed in this thesis consider both task-specific and task-generic approaches to reducing the data requirement for inference, and evaluate our proposed methods on a wide range of computer vision tasks. This thesis makes four distinct contributions: (i) We investigate multi-class action classification via two-stream convolutional neural networks that directly ingest information extracted from compressed video bitstreams. We show that selective access to macroblock motion vector information provides a good low-dimensional approximation of the underlying optical flow in visual sequences. (ii) We devise a bitstream cropping method by which AVC/H.264 and H.265 bitstreams are reduced to the minimum amount of necessary elements for optical flow extraction, while maintaining compliance with codec standards. We additionally study the effect of codec rate-quality control on the sparsity and noise incurred on optical flow derived from resulting bitstreams, and do so for multiple coding standards. (iii) We demonstrate degrees of variability in the amount of data required for action classification, and leverage this to reduce the dimensionality of input volumes by inferring the required temporal extent for accurate classification prior to processing via learnable machines. (iv) We extend the Mixtures-of-Experts (MoE) paradigm to adapt the data cost of inference for any set of constituent experts. We postulate that the minimum acceptable data cost of inference varies for different input space partitions, and consider mixtures where each expert is designed to meet a different set of constraints on input dimensionality. To take advantage of the flexibility of such mixtures in processing different input representations and modalities, we train biased gating functions such that experts requiring less information to make their inferences are favoured to others. We finally note that, our proposed data utility optimization solutions include a learnable component which considers specified priorities on the amount of information to be used prior to inference, and can be realized for any combination of tasks, modalities, and constraints on available data

On the Performance Evaluation of Action Recognition Models on Transcoded Low Quality Videos

In the design of action recognition models, the quality of videos in the dataset is an important issue, however the trade-off between the quality and performance is often ignored. In general, action recognition models are trained and tested on high-quality videos, but in actual situations where action recognition models are deployed, sometimes it might not be assumed that the input videos are of high quality. In this study, we report qualitative evaluations of action recognition models for the quality degradation associated with transcoding by JPEG and H.264/AVC. Experimental results are shown for evaluating the performance of pre-trained models on the transcoded validation videos of Kinetics400. The models are also trained on the transcoded training videos. From these results, we quantitatively show the degree of degradation of the model performance with respect to the degradation of the video quality.Comment: 10 page

LIDAR data classification and compression

Airborne Laser Detection and Ranging (LIDAR) data has a wide range of applications in agriculture, archaeology, biology, geology, meteorology, military and transportation, etc. LIDAR data consumes hundreds of gigabytes in a typical day of acquisition, and the amount of data collected will continue to grow as sensors improve in resolution and functionality. LIDAR data classification and compression are therefore very important for managing, visualizing, analyzing and using this huge amount of data. Among the existing LIDAR data classification schemes, supervised learning has been used and can obtain up to 96% of accuracy. However some of the features used are not readily available, and the training data is also not always available in practice. In existing LIDAR data compression schemes, the compressed size can be 5%-23% of the original size, but still could be in the order of gigabyte, which is impractical for many applications. The objectives of this dissertation are (1) to develop LIDAR classification schemes that can classify airborne LIDAR data more accurately without some features or training data that existing work requires; (2) to explore lossy compression schemes that can compress LIDAR data at a much higher compression rate than is currently available. We first investigate two independent ways to classify LIDAR data depending on the availability of training data: when training data is available, we use supervised machine learning techniques such as support vector machine (SVM); when training data is not readily available, we develop an unsupervised classification method that can classify LIDAR data as good as supervised classification methods. Experimental results show that the accuracy of our classification results are over 99%. We then present two new lossy LIDAR data compression methods and compare their performance. The first one is a wavelet based compression scheme while the second one is geometry based. Our new geometry based compression is a geometry and statistics driven LIDAR point-cloud compression method which combines both application knowledge and scene content to enable fast transmission from the sensor platform while preserving the geometric properties of objects within a scene. The new algorithm is based on the idea of compression by classification. It utilizes the unique height function simplicity as well as the local spatial coherence and linearity of the aerial LIDAR data and can automatically compress the data to the desired level-of-details defined by the user. Either of the two developed classification methods can be used to automatically detect regions that are not locally linear such as vegetations or trees. In those regions, the local statistics descriptions, such as mean, variance, expectation, etc., are stored to efficiently represent the region and restore the geometry in the decompression phase. The new geometry-based compression schemes for building and ground data can compress efficiently and significantly reduce the file size, while retaining a good fit for the scalable "zoom in" requirements. Experimental results show that compared with existing LIDAR lossy compression work, our proposed approach achieves two orders of magnitude lower bit rate with the same quality, making it feasible for applications that were not practical before. The ability to store information into a database and query them efficiently becomes possible with the proposed highly efficient compression scheme.Includes bibliographical references (pages 106-116)