In this paper we present a simple yet effective approach to extend without supervision any object proposal from static images to videos. Unlike previous methods, these spatio-temporal proposals, to which we refer as tracks, are generated relying on little or no visual content by only exploiting bounding boxes spatial correlations through time. The tracks that we obtain are likely to represent objects and are a general-purpose tool to represent meaningful video content for a wide variety of tasks. For unannotated videos, tracks can be used to discover content without any supervision. As further contribution we also propose a novel and dataset-independent method to evaluate a generic object proposal based on the entropy of a classifier output response. We experiment on two competitive datasets, namely YouTube Objects and ILSVRC-2015 VID.
Object proposals @cite_3 provide a relatively small set of bounding boxes likely to contain salient regions in images, based on some measure. Different proposals, such as EdgeBoxes @cite_10 , are commonly used in image related tasks to reduce the number of candidate regions to evaluate. Recently, there have been some attempts to adapt the paradigm of object proposals to videos to solve specific tasks, by generating consistent spatio-temporal volumes. In @cite_4 motion segmentation is exploited to extract a single spatio-temporal tube for video, in order to perform video classification. The task of object discovery is tackled in @cite_7 by generating a set of boxes using a foreground estimation method and matching them across frames using both geometric and appearance terms. Kwak al @cite_1 combine a discovery step matching similar regions in different frames and a tracking step to obtain temporal proposals. In @cite_11 a classifier is learnt to guide a super-voxel merging process for obtaining object proposals. Temporal proposals have been exploited to segment objects in videos in @cite_9 by discovering easy instances and propagating the tube to adjacent frames. Other methods to generate salient tubes have been proposed for action localization in @cite_0 using human and motion detection.
@cite_4 @cite_7 @cite_9 @cite_1 @cite_3 @cite_0 @cite_10 @cite_11
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We present an unsupervised approach that generates a diverse, ranked set of bounding box and segmentation video object proposals—spatio-temporal tubes that localize the foreground objects—in an unannotated video. In contrast to previous unsupervised methods that either track regions initialized in an arbitrary frame or train a fixed model over a cluster of regions, we instead discover a set of easy-togroup instances of an object and then iteratively update its appearance model to gradually detect harder instances in temporally-adjacent frames. Our method first generates a set of spatio-temporal bounding box proposals, and then refines them to obtain pixel-wise segmentation proposals. We demonstrate state-of-the-art segmentation results on the SegTrack v2 dataset, and bounding box tracking results that perform competitively to state-of-the-art supervised tracking methods. In this paper, we propose a novel approach to extract primary object segments in videos in the object proposal' domain. The extracted primary object regions are then used to build object models for optimized video segmentation. The proposed approach has several contributions: First, a novel layered Directed Acyclic Graph (DAG) based framework is presented for detection and segmentation of the primary object in video. We exploit the fact that, in general, objects are spatially cohesive and characterized by locally smooth motion trajectories, to extract the primary object from the set of all available proposals based on motion, appearance and predicted-shape similarity across frames. Second, the DAG is initialized with an enhanced object proposal set where motion based proposal predictions (from adjacent frames) are used to expand the set of object proposals for a particular frame. Last, the paper presents a motion scoring function for selection of object proposals that emphasizes high optical flow gradients at proposal boundaries to discriminate between moving objects and the background. The proposed approach is evaluated using several challenging benchmark videos and it outperforms both unsupervised and supervised state-of-the-art methods. We segment moving objects in videos by ranking spatio-temporal segment proposals according to “moving objectness”; how likely they are to contain a moving object. In each video frame, we compute segment proposals using multiple figure-ground segmentations on per frame motion boundaries. We rank them with a Moving Objectness Detector trained on image and motion fields to detect moving objects and discard over under segmentations or background parts of the scene. We extend the top ranked segments into spatio-temporal tubes using random walkers on motion affinities of dense point trajectories. Our final tube ranking consistently outperforms previous segmentation methods in the two largest video segmentation benchmarks currently available, for any number of proposals. Further, our per frame moving object proposals increase the detection rate up to 7 over previous state-of-the-art static proposal methods. We segment moving objects in videos by ranking spatio-temporal segment proposals according to "moving objectness": how likely they are to contain a moving object. In each video frame, we compute segment proposals using multiple figure-ground segmentations on per frame motion boundaries. We rank them with a Moving Objectness Detector trained on image and motion fields to detect moving objects and discard over under segmentations or background parts of the scene. We extend the top ranked segments into spatio-temporal tubes using random walkers on motion affinities of dense point trajectories. Our final tube ranking consistently outperforms previous segmentation methods in the two largest video segmentation benchmarks currently available, for any number of proposals. Further, our per frame moving object proposals increase the detection rate up to 7 over previous state-of-the-art static proposal methods.