Abstract

Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the need for annotated datasets by proposing an unsupervised re-identification network, thus sidestepping the labeling costs entirely, required for training. Given unlabeled videos, our proposed method (SimpleReID) first generates tracking labels using SORT [ 3] and trains a ReID network to predict the generated labels using crossentropy loss. We demonstrate that SimpleReID performs substantially better than simpler alternatives, and we recover the full performance of its supervised counterpart consistently across diverse tracking frameworks. The observations are unusual because unsupervised ReID is not expected to excel in crowded scenarios with occlusions, and drastic viewpoint changes. By incorporating our unsupervised SimpleReID with CenterTrack trained on augmented still images, we establish a new state-of-the-art performance on popular datasets like MOT16/17 without using tracking supervision, beating current best (CenterTrack) by 0.2-0.3 MOTA and 4.4-4.8 IDF1 scores. We further provide evidence for limited scope for improvement in IDF1 scores beyond our unsupervised ReID in the studied settings. Our investigation suggests reconsideration towards more sophisticated, supervised, end-to-end trackers [56 , 5] by showing promise in simpler unsupervised alternatives.

Abstract

We present GAZED– eye GAZ-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational applications as a cue to capture interesting scene content; we employ gaze as a proxy to select shots for inclusion in the edited video. Given the original video, scene content and user eye-gaze tracks are combined to generate an edited video comprising of cinematically valid actor shots and shot transitions to generate an aesthetic and vivid representation of the original narrative. We model cinematic video editing as an energy minimization problem over shot selection, whose constraints capture cinematographic editing conventions. Gazed scene locations primarily determine the shots constituting the edited video. Effectiveness of GAZED against multiple competing methods is demonstrated via a psychophysical study involving 12 users and twelve performance videos. Professional video recordings of stage performances are typically created by employing skilled camera operators, who record the performance from multiple viewpoints. These multi-camera feeds, termed rushes, are then edited together to portray an eloquent story intended to maximize viewer engagement. Generating professional edits of stage performances is both difficult and challenging. Firstly, maneuvering cameras during a live performance is difficult even for experts as there is no option of retake upon error, and camera viewpoints are limited as the use of large supporting equipment (trolley, crane .) is infeasible. Secondly, manual video editing is an extremely slow and tedious process and leverages the experience of skilled editors. Overall, the need for (i) a professional camera crew, (ii) multiple cameras and supporting equipment, and (iii) expert editors escalates the process complexity and costs. Consequently, most production houses employ a large field-of-view static camera, placed far enough to capture the entire stage. This approach is widespread as it is simple to implement, and also captures the entire scene. Such static visualizations are apt for archival purposes; however, they are often unsuccessful at captivating attention when presented to the target audience. While conveying the overall context, the distant camera feed fails to bring out vivid scene details like close-up faces, character emotions and actions, and ensuing interactions which are critical for cinematic storytelling. GAZED denotes an end-to-end pipeline to generate an aesthetically edited video from a single static, wide-angle stage recording. This is inspired by prior work [GRG14], which describes how a plural camera crew can be replaced by a single highresolution static camera, and multiple virtual camera shots or rushes generated by simulating several virtual pan/tilt/zoom cameras to focus on actors and actions within the original recording. In this work, we demonstrate that the multiple rushes can be automatically edited by leveraging user eye gaze information, by modeling (virtual) shot selection as a discrete optimization problem. Eye-gaze represents an inherent guiding factor for video editing, as eyes are sensitive to interesting scene events [RKH∗ 09, SSSM14] that need to be vividly presented in the edited video. The objective critical for video editing and the key contribution of our work is to decide which shot (or rush) needs to be selected to describe each frame of the edited video. The shot selection problem is modeled as an optimization, which incorporates gaze information along with other cost terms that model cinematic editing principles. Gazed scene locations are utilized to define gaze potentials, which measure the importance of the different shots to choose from. Gaze potentials are then combined with other terms that model cinematic principles like avoiding jump cuts (which produce jarring shot transitions), rhythm (pace of shot transitioning), avoiding transient shots . The optimization is solved using dynamic programming. [MKSG20] refers to the detailed full article.

Abstract

Colorization is a complex task of selecting a combination of colors and arriving at an appropriate spatial arrangement of the colors in an image. In this paper, we propose a novel approach for automatic colorization of graphic arts like graphic patterns, info-graphics and cartoons. Our approach uses the artist’s colored graphics as a reference to color a template image. We also propose a retrieval system for selecting a relevant reference image corresponding to the given template from a dataset of reference images colored by different artists. Finally, we formulate the problem of colorization as a optimal graph matching problem over color groups in the reference and the template image. We demonstrate results on a variety of coloring tasks and evaluate our model through multiple perceptual studies. The studies show that the results generated through our model are significantly preferred by the participants over other automatic colorization methods.

Abstract

Abstract— Detecting small obstacles on the road is critical for autonomous driving. In this paper, we present a method to reliably detect such obstacles through a multi-modal framework of sparse LiDAR(VLP-16) and Monocular vision. LiDAR is employed to provide additional context in the form of confidence maps to monocular segmentation networks. We show significant performance gains when the context is fed as an additional input to monocular semantic segmentation frameworks. We further present a new semantic segmentation dataset to the community, comprising of over 3000 image frames with corresponding LiDAR observations. The images come with pixel-wise annotations of three classes off-road, road, and small obstacle. We stress that precise calibration between LiDAR and camera is crucial for this task and thus propose a novel Hausdorff distance based calibration refinement method over extrinsic parameters. As a first benchmark over this dataset, we report our results with 73 % instance detection up to a distance of 50 meters on challenging scenarios. Qualitatively by showcasing accurate segmentation of obstacles less than 15 cms at 50m depth and quantitatively through favourable comparisons vis a vis prior art, we vindicate the method’s efficacy. Our project and dataset is hosted at https://small-obstacle-dataset.github.io/

Abstract

Abstract— Learning computational models for visual attention (saliency estimation) is an effort to inch machines/robots closer to human visual cognitive abilities. Data-driven efforts have dominated the landscape since the introduction of deep neural network architectures. In deep learning research, the choices in architecture design are often empirical and frequently lead to more complex models than necessary. The complexity, in turn, hinders the application requirements. In this paper, we identify four key components of saliency models, i.e., input features, multi-level integration, readout architecture, and loss functions. We review the existing state of the art models on these four components and propose novel and simpler alternatives. As a result, we propose two novel end-to-end architectures called SimpleNet and MDNSal, which are neater, minimal, more interpretable and achieve state of the art performance on public saliency benchmarks. SimpleNet is an optimized encoder-decoder architecture and brings notable performance gains on the SALICON dataset (the largest saliency benchmark). MDNSal is a parametric model that directly predicts parameters of a GMM distribution and is aimed to bring more interpretability to the prediction maps. The proposed saliency models can be inferred at 25fps, making them suitable for realtime applications. Code and pre-trained models are available at https://github.com/samyak0210/saliency.

Abstract

Abstract In this work, we propose an no-reference image quality assessment (NR-IQA) approach at a confluence of signal processing and deep learning. We use MTF50 (spatial frequency where modulation transfer function is 50% of its peak value) on slanted edged as a measure for image quality. We propose a comprehensive IQA dataset of images captured through hand-held phone camera in variety of situations with slanted edges around it. The MTF50 values at the slanted edges are then used to garner ground truth values for each patch in the captured images. A convolution neural network is then trained to predict MTF50 values from arbitrary image patches. We present results on the proposed dataset and synthetically generated TID2013 dataset and show state-of-the-art performance for IQA in the wild.

Abstract

Recent works have proposed several long term tracking benchmarks and highlight the importance of moving towards long-duration tracking to bridge the gap with application requirements. The current evaluation methodologies, however, do not focus on several aspects that are crucial in a long term perspective like Re-detection, Recovery, and Reliability. In this paper, we propose novel evaluation strategies for a more in-depth analysis of trackers from a long-term perspective. More specifically, (a) we test redetection capability of the trackers in the wild by simulating virtual cuts, (b) we investigate the role of chance in the recovery of tracker after failure and (c) we propose a novel metric allowing visual inference on the ability of a tracker to track contiguously (without any failure) at a given accuracy. We present several original insights derived from an extensive set of quantitative and qualitative experiments.

Abstract

Autonomous camera systems are often subjected to an optimization/filtering operation to smoothen and stabilize the rough trajectory estimates. Most common filtering techniques do reduce the irregularities in data; however, they fail to mimic the behavior of a human cameraman. Global filtering methods modeling human camera operators have been successful; however, they are limited to offline settings. In this paper, we propose two online filtering methods called Cinefilters, which produce smooth camera trajectories that are motivated by cinematographic principles. The first filter (CineConvex) uses a sliding window-based convex optimization formulation, and the second (CineCNN) is a CNN based encoder-decoder model. We evaluate the proposed filters in two different settings, namely a basketball dataset and a stage performance dataset. Our models outperform previous methods and baselines on both quantitative and qualitative metrics. The CineConvex and CineCNN filters operate at about 250fps and 1000fps, respectively, with a minor latency (half a second), making them apt for a variety of real-time applications.

Abstract

In this paper, we present a simple baseline for visual grounding for autonomous driving which outperforms the state of the art methods, while retaining minimal design choices. Our framework minimizes the cross-entropy loss over the cosine distance between multiple image ROI features with a text embedding (representing the given sentence/phrase). We use pre-trained networks for obtaining the initial embeddings and learn a transformation layer on top of the text embedding. We perform experiments on the Talk2Car dataset and achieve 68.7% AP50 accuracy, improving upon the previous state of the art by 8.6%. Our investigation suggests reconsideration towards more approaches employing sophisticated attention mechanisms or multi-stage reasoning or complex metric learning loss functions by showing promise in simpler alternatives.

Abstract

We present GAZED-eye GAZe-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational applications as a cue to capture interesting scene content; we employ gaze as a proxy to select shots for inclusion in the edited video. Given the original video, scene content and user eye-gaze tracks are combined to generate an edited video comprising cinematically valid actor shots and shot transitions to generate an aesthetic and vivid representation of the original narrative. We model cinematic video editing as an energy minimization problem over shot selection, whose constraints capture cinematographic editing conventions. Gazed scene locations primarily determine the shots constituting the edited video. Effectiveness of GAZED against multiple competing methods is demonstrated via a psychophysical study involving 12 …