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.
In this work we tackle the video geolocation problem through image geolocation techniques, and cast it as an image retrieval task. The problem of image place recognition has been studied extensively @cite_9 , @cite_10 , @cite_4 , @cite_14 , @cite_18 , @cite_12 , @cite_17 , @cite_16 . In these proposed methods, usually the query image location is estimated using the locations of the most visually similar images retrieved from a large geotagged database.
@cite_18 @cite_14 @cite_4 @cite_9 @cite_16 @cite_10 @cite_12 @cite_17
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We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day night), change of seasons, aging, or structural modifications over time such as buildings being built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact indexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly outperforms other large-scale place recognition techniques on this challenging data. We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact indexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly outperforms other large-scale place recognition techniques on this challenging data. The problem of localization and geo-location estimation of an image has a long-standing history both in robotics and computer vision. With the advent of availability of large amounts of geo-referenced image data, several image retrieval approaches have been deployed to tackle this problem. In this work, we will show how the capability of semantic labeling of both query views and the reference dataset by means of semantic segmentation can aid (1) the problem of retrieval of views similar and possibly overlapping with the query and (2) guide the recognition and discovery of commonly occurring scene layouts in the reference dataset. We will demonstrate the effectiveness of these semantic representations on examples of localization, semantic concept discovery, and intersection recognition in the images of urban scenes. The recognition of a place depicted in an image typically adopts methods from image retrieval in large-scale databases. First, a query image is described as a “bag-of-features” and compared to every image in the database. Second, the most similar images are passed to a geometric verification stage. However, this is an inefficient approach when considering that some database images may be almost identical, and many image features may not repeatedly occur. We address this issue by clustering similar database images to represent distinct scenes, and tracking local features that are consistently detected to form a set of real-world landmarks. Query images are then matched to landmarks rather than features, and a probabilistic model of landmark properties is learned from the cluster to appropriately verify or reject putative feature matches. We present novelties in both a bag-of-features retrieval and geometric verification stage based on this concept. Results on a database of 200K images of popular tourist destinations show improvements in both recognition performance and efficiency compared to traditional image retrieval methods.