Comprehensive visual understanding requires detection frameworks that can effectively learn and utilize object interactions while analyzing objects individually. This is the main objective in Human-Object Interaction (HOI) detection task. In particular, relative spatial reasoning and structural connections between objects are essential cues for analyzing interactions, which is addressed by the proposed Visual-Spatial-Graph Network (VSGNet) architecture. VSGNet extracts visual features from the human-object pairs, refines the features with spatial configurations of the pair, and utilizes the structural connections between the pair via graph convolutions. The performance of VSGNet is thoroughly evaluated using the Verbs in COCO (V-COCO) dataset. Experimental results indicate that VSGNet outperforms state-of-the-art solutions by 8% or 4 mAP.

Building successful collaboration between humans and robots requires efficient, effective, and natural communication. Here we study a RGB-based deep learning approach for controlling robots through gestures (e.g., "follow me"). To address the challenge of collecting high-quality annotated data from human subjects, synthetic data is considered for this domain. We contribute a dataset of gestures that includes real videos with human subjects and synthetic videos from our custom simulator. A solution is presented for gesture recognition based on the state-of-the-art I3D model. Comprehensive testing was conducted to optimize the parameters for this model. Finally, to gather insight on the value of synthetic data, several experiments are described that systematically study the properties of synthetic data (e.g., gesture variations, character variety, generalization to new gestures). We discuss practical implications for the design of effective human-robot collaboration and the usefulness of synthetic data for deep learning.

Effective analysis of hyperspectral imagery is essential for gathering fast and actionable information of large areas affected by atmospheric and green house gases. Existing methods, which process hyperspectral data to detect amorphous gases such as CH4require manual inspection from domain experts and annotation of massive datasets. These methods do not scale well and are prone to human errors due to the plumes’ small pixel-footprint signature. The proposed Hyperspectral Mask-RCNN (H-mrcnn) uses principled statistics, signal processing, and deep neural networks to address these limitations. H-mrcnn introduces fast algorithms to analyze large-area hyper-spectral information and methods to autonomously represent and detect CH4plumes. H-mrcnn processes information by match-filtering sliding windows of hyperspectral data across the spectral bands. This process produces information-rich features that are both effective plume representations and gas concentration analogs. The optimized matched-filtering stage processes spectral data, which is spatially sampled to train an ensemble of gas detectors. The ensemble outputs are fused to estimate a natural and accurate plume mask. Thorough evaluation demonstrates that H-mrcnn matches the manual and experience-dependent annotation process of experts by 85% (IOU). H-mrcnn scales to larger datasets, reduces the manual data processing and labeling time (×12), and produces rapid actionable information about gas plumes.

While observing complex events with multiple actors, humans do not assess each actor separately, but infer from the context. The surrounding context provides essential information for understanding actions. To this end, we propose to replace region of interest(RoI) pooling with an attention module, which ranks each spatio-temporal region's relevance to a detected actor instead of cropping. We refer to these as Actor-Conditioned Attention Maps (ACAM), which amplify/dampen the features extracted from the entire scene. The resulting actor-conditioned features focus the model on regions that are relevant to the conditioned actor. For actor localization, we leverage pre-trained object detectors, which transfer better. The proposed model is efficient and our action detection pipeline achieves near real-time performance. Experimental results on AVA 2.1 and JHMDB demonstrate the effectiveness of attention maps, with improvements of 7 mAP on AVA and 4 mAP on JHMDB.

Detecting activities from video taken with a single camera is an active research area for ML-based machine vision. In this paper, we examine the next research frontier: near real-time detection of complex activities spanning multiple (possibly wireless) cameras, a capability applicable to surveillance tasks. We argue that a system for such complex activity detection must employ a hybrid design: one in which rule-based activity detection must complement neural network based detection. Moreover, to be practical, such a system must scale well to multiple cameras and have low end-to-end latency. Caesar, our edge computing based system for complex activity detection, provides an extensible vocabulary of activities to allow users to specify complex actions in terms of spatial and temporal relationships between actors, objects, and activities. Caesar converts these specifications to graphs, efficiently monitors camera feeds, partitions processing between cameras and the edge cluster, retrieves minimal information from cameras, carefully schedules neural network invocation, and efficiently matches specification graphs to the underlying data in order to detect complex activities. Our evaluations show that Caesar can reduce wireless bandwidth, on-board camera memory, and detection latency by an order of magnitude while achieving good precision and recall for all complex activities on a public multi-camera dataset.

We propose a new order preserving bilinear framework that exploits low-resolution video for person detection in a multi-modal setting using deep neural networks. In this setting cameras are strategically placed such that less robust sensors, e.g. geophones that monitor seismic activity, are located within the field of views (FOVs) of cameras. The primary challenge is being able to leverage sufficient information from videos where there are less than 40 pixels on targets, while also taking advantage of less discriminative information from other modalities, e.g. seismic. Unlike state-of-the-art methods, our bilinear framework retains spatio-temporal order when computing the vector outer products between pairs of features. Despite the high dimensionality of these outer products, we demonstrate that our order preserving bilinear framework yields better performance than recent orderless bilinear models and alternative fusion methods. Code is available at https://github.com/oulutan/OP-Bilinear-Model.