Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks

Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems and semantic crawlers of large video repositories, the video capture and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification models that directly ingest AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video bitstreams and applying complex optical flow calculations prior to CNN processing, we only retain motion vector and select texture information at significantly-reduced bitrates and apply no additional processing prior to CNN ingestion. Based on three CNN architectures and two action recognition datasets, we achieve 11%-94% saving in bitrate with marginal effect on classification accuracy. A model-based selection between multiple CNNs increases these savings further, to the point where, if up to 7% loss of accuracy can be tolerated, video classification can take place with as little as 3 kbps for the transport of the required compressed video information to the system implementing the CNN models

Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks

Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems and semantic crawlers of large video repositories, the video capture and the CNN-based semantic analysis parts do not tend to be colocated. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification models that directly ingest AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video bitstreams and applying complex optical flow calculations prior to CNN processing, we only retain motion vector and select texture information at significantly-reduced bitrates and apply no additional processing prior to CNN ingestion. Based on three CNN architectures and two action recognition datasets, we achieve 11%–94% saving in bitrate with marginal effect on classification accuracy. A model-based selection between multiple CNNs increases these savings further, to the point where, if up to 7% loss of accuracy can be tolerated, video classification can take place with as little as 3 kbps for the transport of the required compressed video information to the system implementing the CNN models

Rate-accuracy trade-off in video classification with deep convolutional neural networks

Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet -of- Things applications, surveillance systems and semantic crawlers of large video repositories, the compressed video content and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification that ingests AVC/H.264 encoded videos. Instead of entire compressed video bitstreams, we only retain motion vector and selected texture information at significantly reduced bitrates. Based on two CNN architectures and two action recognition datasets, we achieve 38%-59% saving in bitrate with marginal impact in classification accuracy. A simple rate-based selection between the two CNNs shows that even further bitrate savings are possible with graceful degradation in accuracy. This may allow for rate/accuracy-optimized CNN-based video classification over networks

From Pixels to Spikes: Efficient Multimodal Learning in the Presence of Domain Shift

Computer vision aims to provide computers with a conceptual understanding of images or video by learning a high-level representation. This representation is typically derived from the pixel domain (i.e., RGB channels) for tasks such as image classification or action recognition. In this thesis, we explore how RGB inputs can either be pre-processed or supplemented with other compressed visual modalities, in order to improve the accuracy-complexity tradeoff for various computer vision tasks. Beginning with RGB-domain data only, we propose a multi-level, Voronoi based spatial partitioning of images, which are individually processed by a convolutional neural network (CNN), to improve the scale invariance of the embedding. We combine this with a novel and efficient approach for optimal bit allocation within the quantized cell representations. We evaluate this proposal on the content-based image retrieval task, which constitutes finding similar images in a dataset to a given query. We then move to the more challenging domain of action recognition, where a video sequence is classified according to its constituent action. In this case, we demonstrate how the RGB modality can be supplemented with a flow modality, comprising motion vectors extracted directly from the video codec. The motion vectors (MVs) are used both as input to a CNN and as an activity sensor for providing selective macroblock (MB) decoding of RGB frames instead of full-frame decoding. We independently train two CNNs on RGB and MV correspondences and then fuse their scores during inference, demonstrating faster end-to-end processing and competitive classification accuracy to recent work. In order to explore the use of more efficient sensing modalities, we replace the MV stream with a neuromorphic vision sensing (NVS) stream for action recognition. NVS hardware mimics the biological retina and operates with substantially lower power and at significantly higher sampling rates than conventional active pixel sensing (APS) cameras. Due to the lack of training data in this domain, we generate emulated NVS frames directly from consecutive RGB frames and use these to train a teacher-student framework that additionally leverages on the abundance of optical flow training data. In the final part of this thesis, we introduce a novel unsupervised domain adaptation method for further minimizing the domain shift between emulated (source) and real (target) NVS data domains