Speedy abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on inherent redundancy of video structures, we propose an efficient sparse combination learning framework. It achieves decent performance in the detection phase without compromising result quality. The short running time is guaranteed because the new method effectively turns the original complicated problem to one in which only a few costless small-scale least square optimization steps are involved. Our method reaches high detection rates on benchmark datasets at a speed of 140-150 frames per second on average when computing on an ordinary desktop PC using MATLAB.

/pdf/abnormal-event-detection-at-150-fps-in-matlab-2hd6b50jp8.pdf

Abnormal Event Detection at 150 FPS in MATLAB

Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns (termed as regularity) using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build a fully convolutional feed-forward autoencoder to learn both the local features and the classifiers as an end-to-end learning framework. Our model can capture the regularities from multiple datasets. We evaluate our methods in both qualitative and quantitative ways - showing the learned regularity of videos in various aspects and demonstrating competitive performance on anomaly detection datasets as an application.

Learning Temporal Regularity in Video Sequences

The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results.

/pdf/anomaly-detection-and-localization-in-crowded-scenes-qkc6y7r8q4.pdf

Anomaly Detection and Localization in Crowded Scenes

We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.

/pdf/learning-deep-representations-of-appearance-and-motion-for-3iln37g6qy.pdf

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

Anomalous events detection in real-world video scenes is a challenging problem due to the complexity of "anomaly" as well as the cluttered backgrounds, objects and motions in the scenes. Most existing methods use hand-crafted features in local spatial regions to identify anomalies. In this paper, we propose a novel model called Spatio-Temporal AutoEncoder (ST AutoEncoder or STAE), which utilizes deep neural networks to learn video representation automatically and extracts features from both spatial and temporal dimensions by performing 3-dimensional convolutions. In addition to the reconstruction loss used in existing typical autoencoders, we introduce a weight-decreasing prediction loss for generating future frames, which enhances the motion feature learning in videos. Since most anomaly detection datasets are restricted to appearance anomalies or unnatural motion anomalies, we collected a new challenging dataset comprising a set of real-world traffic surveillance videos. Several experiments are performed on both the public benchmarks and our traffic dataset, which show that our proposed method remarkably outperforms the state-of-the-art approaches.

Spatio-Temporal AutoEncoder for Video Anomaly Detection

Anomalies in many video surveillance applications have local spatio-temporal signatures, namely, they occur over a small time window or a small spatial region. The distinguishing feature of these scenarios is that outside this spatio-temporal anomalous region, activities appear normal. We develop a probabilistic framework to account for such local spatio-temporal anomalies. We show that our framework admits elegant characterization of optimal decision rules. A key insight of the paper is that if anomalies are local optimal decision rules are local even when the nominal behavior exhibits global spatial and temporal statistical dependencies. This insight helps collapse the large ambient data dimension for detecting local anomalies. Consequently, consistent data-driven local empirical rules with provable performance can be derived with limited training data. Our empirical rules are based on scores functions derived from local nearest neighbor distances. These rules aggregate statistics across spatio-temporal locations & scales, and produce a single composite score for video segments. We demonstrate the efficacy of our scheme on several video surveillance datasets and compare with existing work.

/pdf/video-anomaly-detection-based-on-local-statistical-5fb83t4eip.pdf

Video anomaly detection based on local statistical aggregates

In recent years, deep learning-based approaches have shown great strength in multi-view stereo because of their outstanding ability to extract robust visual features. However, most learning-based methods need to build the cost volume and increase the receptive field enormously to get a satisfactory result when dealing with large-scale textureless regions, consequently leading to prohibitive memory consumption. To ensure both memory-friendly and textureless-resilient, we innovatively transplant the spirit of deformable convolution from deep learning into the traditional PatchMatch-based method. Specifically, for each pixel with matching ambiguity (termed unreliable pixel), we adaptively deform the patch centered on it to extend the receptive field until covering enough correlative reliable pixels (without matching ambiguity) that serve as anchors. When performing PatchMatch, constrained by the anchor pixels, the matching cost of an unreliable pixel is guaranteed to reach the global minimum at the correct depth and therefore increases the robustness of multi-view stereo significantly. To detect more anchor pixels to ensure better adaptive patch deformation, we propose to evaluate the matching ambiguity of a certain pixel by checking the convergence of the estimated depth as optimization proceeds. As a result, our method achieves state-of-the-art performance on ETH3D and Tanks and Temples while preserving low memory consumption. 

Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo

Existing self-supervised multi-view stereo (MVS) approaches largely rely on photometric consistency for geometry inference, and hence suffer from low-texture or non-Lambertian appearances. In this paper, we observe that adjacent geometry shares certain commonality that can help to infer the correct geometry of the challenging or low-confident regions. Yet exploiting such property in a non-supervised MVS approach remains challenging for the lacking of training data and necessity of ensuring consistency between views. To address the issues, we propose a novel geometry inference training scheme by selectively masking regions with rich textures, where geometry can be well recovered and used for supervisory signal, and then lead a deliberately designed cost volume completion network to learn how to recover geometry of the masked regions. During inference, we then mask the low-confident regions instead and use the cost volume completion network for geometry correction. To deal with the different depth hypotheses of the cost volume pyramid, we design a three-branch volume inference structure for the completion network. Further, by considering plane as a special geometry, we first identify planar regions from pseudo labels and then correct the low-confident pixels by high-confident labels through plane normal consistency. Extensive experiments on DTU and Tanks & Temples demonstrate the effectiveness of the proposed framework and the state-of-the-art performance.

Self-Supervised Multi-view Stereo via Adjacent Geometry Guided Volume Completion

EpiLoc: Deep Camera Localization Under Epipolar Constraint

Monocular face reconstruction is a significant task in many multimedia applications. However, learning-based methods unequivocally suffer from the lack of large datasets annotated with 3-D ground truth. To tackle this problem, we proposed a novel end-to-end 3-D face reconstruction network consisting of a domain-transfer conditional GAN (cGAN) and a face reconstruction network. Our method first uses cGAN to translate the realistic face images to the specific rendered style, with a novel 2-D facial edge consistency loss function to exploit in-the-wild images. The domain-transferred images are then fed into a 3-D face reconstruction network. We further propose a novel reprojection consistency loss to restrict the 3-D face reconstruction network in a self-supervised way. Our approach can be trained with the annotated dataset, synthetic dataset, and in-the-wild images to learn a unified face model. Extensive experiments have demonstrated the effectiveness of our method.

Learning 3-D Face Shape From Diverse Sources With Cross-Domain Face Synthesis

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