Efficient multi-level scene understanding in videos

Automatic video parsing is a key step towards human-level dynamic scene understanding, and a fundamental problem in computer vision. A core issue in video understanding is to infer multiple scene properties of a video in an efficient and consistent manner. This thesis addresses the problem of holistic scene understanding from monocular videos, which jointly reason about semantic and geometric scene properties from multiple levels, including pixelwise annotation of video frames, object instance segmentation in spatio-temporal domain, and/or scene-level description in terms of scene categories and layouts. We focus on four main issues in the holistic video understanding: 1) what is the representation for consistent semantic and geometric parsing of videos? 2) how do we integrate high-level reasoning (e.g., objects) with pixel-wise video parsing? 3) how can we do efficient inference for multi-level video understanding? and 4) what is the representation learning strategy for efficient/cost-aware scene parsing? We discuss three multi-level video scene segmentation scenarios based on different aspects of scene properties and efficiency requirements. The first case addresses the problem of consistent geometric and semantic video segmentation for outdoor scenes. We propose a geometric scene layout representation, or a stage scene model, to efficiently capture the dependency between the semantic and geometric labels. We build a unified conditional random field for joint modeling of the semantic class, geometric label and the stage representation, and design an alternating inference algorithm to minimize the resulting energy function. The second case focuses on the problem of simultaneous pixel-level and object-level segmentation in videos. We propose to incorporate foreground object information into pixel labeling by jointly reasoning semantic labels of supervoxels, object instance tracks and geometric relations between objects. In order to model objects, we take an exemplar approach based on a small set of object annotations to generate a set of object proposals. We then design a conditional random field framework that jointly models the supervoxel labels and object instance segments. To scale up our method, we develop an active inference strategy to improve the efficiency of multi-level video parsing, which adaptively selects an informative subset of object proposals and performs inference on the resulting compact model. The last case explores the problem of learning a flexible representation for efficient scene labeling. We propose a dynamic hierarchical model that allows us to achieve flexible trade-offs between efficiency and accuracy. Our approach incorporates the cost of feature computation and model inference, and optimizes the model performance for any given test-time budget. We evaluate all our methods on several publicly available video and image semantic segmentation datasets, and demonstrate superior performance in efficiency and accuracy. Keywords: Semantic video segmentation, Multi-level scene understanding, Efficient inference, Cost-aware scene parsin

Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling

Self-Supervised Relative Depth Learning for Urban Scene Understanding

As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth. It is natural for a learning agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, faraway mountains don't move much; nearby trees move a lot. This natural relationship between the appearance of objects and their motion is a rich source of information about the world. In this work, we start by training a deep network, using fully automatic supervision, to predict relative scene depth from single images. The relative depth training images are automatically derived from simple videos of cars moving through a scene, using recent motion segmentation techniques, and no human-provided labels. This proxy task of predicting relative depth from a single image induces features in the network that result in large improvements in a set of downstream tasks including semantic segmentation, joint road segmentation and car detection, and monocular (absolute) depth estimation, over a network trained from scratch. The improvement on the semantic segmentation task is greater than those produced by any other automatically supervised methods. Moreover, for monocular depth estimation, our unsupervised pre-training method even outperforms supervised pre-training with ImageNet. In addition, we demonstrate benefits from learning to predict (unsupervised) relative depth in the specific videos associated with various downstream tasks. We adapt to the specific scenes in those tasks in an unsupervised manner to improve performance. In summary, for semantic segmentation, we present state-of-the-art results among methods that do not use supervised pre-training, and we even exceed the performance of supervised ImageNet pre-trained models for monocular depth estimation, achieving results that are comparable with state-of-the-art methods