In this work we propose a method that geolocates videos within a delimited widespread area based solely on the frames visual content. Our proposed method tackles video-geolocation through traditional image retrieval techniques considering Google Street View as the reference point. To achieve this goal we use the deep learning features obtained from NetVLAD to represent images, since through this feature vectors the similarity is their L2 norm. In this paper, we propose a family of voting-based methods to aggregate frame-wise geolocation results which boost the video geolocation result. The best aggregation found through our experiments considers both NetVLAD and SIFT similarity, as well as the geolocation density of the most similar results. To test our proposed method, we gathered a new video dataset from Pittsburgh Downtown area to benefit and stimulate more work in this area. Our system achieved a precision of 90 while geolocating videos within a range of 150 meters or two blocks away from the original position.
It is to our knowledge that only few research projects address the problem of geolocating videos. @cite_6 proposed a web video geolocating algorithm by propagating geotags among the web video social relationship graph. For this reason, their algorithm can only locate videos from social networks. @cite_2 use video content along with various additional signals to infer the location of videos. Their method requires a predefined candidate coarse location set, such as Trafford (UK) and Baghdad (Iraq), where the query video is classified into one of the candidate locations. One limitation of this method is that it restricts the predicted locations to a predefined candidate set. Besides, it only infers the city or town where the video is taken, but cannot give accurate geodetic coordinates.
@cite_6 @cite_2
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This paper considers the problem of web video geolocation: we hope to determine where on the Earth a web video was taken. By analyzing a 6.5-million geotagged web video dataset, we observe that there exist inherent geography intimacies between a video with its relevant videos (related videos and same-author videos). This social relationship supplies a direct and effective cue to locate the video to a particular region on the earth. Based on this observation, we propose an effective web video geolocation algorithm by propagating geotags among the web video social relationship graph. For the video that have no geotagged relevant videos, we aim to collect those geotagged relevant images that are content similar with the video (share some visual or textual information with the video) as the cue to infer the location of the video. The experiments have demonstrated the effectiveness of both methods, with the geolocation accuracy much better than state-of-the-art approaches. Finally, an online web video geolocation system: Video2Locatoin (V2L) is developed to provide public access to our algorithm. The rapid adoption and deployment of ubiquitous video cameras has led to the collection of voluminous amounts of media data. However, indexing and searching of large video databases remain a very challenging task. Recently, some recorded video data are automatically annotated with meta-data collected from various sensors such as Global Positioning System (GPS) and compass devices. In our earlier work, we proposed the notion of a viewable scene model derived from the fusion of location and direction sensor information with a video stream. Such georeferenced media streams are useful in many applications and, very importantly, they can effectively be searched via their meta-data on a large scale. Consequently, search by geo-properties complements traditional content-based retrieval methods. The result of a georeferenced video query will in general consist of a number of video segments that satisfy the query conditions, but with more or less relevance. For example, a building of interest may appear in a video segment, but may only be visible in a corner. Therefore, an essential and integral part of a video query is the ranking of the result set according to the relevance of each clip. An effective result ranking is even more important for video than it is for text search, since the browsing of results can only be achieved by viewing each clip, which is very time consuming. In this study, we investigate and present three ranking algorithms that use spatial and temporal properties of georeferenced videos to effectively rank search results. To allow our techniques to scale to large video databases, we further introduce a histogram-based approach that allows fast online computations. An experimental evaluation demonstrates the utility of the proposed methods. This paper explores the problem of large-scale automatic video geolocation. A methodology is developed to infer the location at which videos from Anonymized.com were recorded using video content and various additional signals. Specifically, multiple binary Adaboost classifiers are trained to identify particular places based on learning decision stumps on sets of hundreds of thousands of sparse features. A one-vs-all classification strategy is then used to classify the location at which videos were recorded. Empirical validation is performed on an immense data set of 20 million labeled videos. Results demonstrate that high accuracy video geolocation is indeed possible for many videos and locations and interesting relationships exist between between videos and the places where they are recorded. The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or “bird's eye” imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations.