Compressive change retrieval for moving object detection

Abstract

Abstract-Change detection, or anomaly detection, from street-view images acquired by an autonomous robot at multiple different times, is a major problem in robotic mapping and autonomous driving. Formulation as an image comparison task, which operates on a given pair of query and reference images is common to many existing approaches to this problem. Unfortunately, providing relevant reference images is not straightforward. In this paper, we propose a novel formulation for change detection, termed compressive change retrieval, which can operate on a query image and similar reference images retrieved from the web. Compared to previous formulations, there are two sources of difficulty. First, the retrieved reference images may frequently contain non-relevant reference images, because even state-of-the-art place-recognition techniques suffer from retrieval noise. Second, image comparison needs to be conducted in a compressed domain to minimize the storage cost of large collections of street-view images. To address the above issues, we also present a practical change detection algorithm that uses compressed bag-of-words (BoW) image representation as a scalable solution. The results of experiments conducted on a practical change detection task, "moving object detection (MOD)," using the publicly available Malaga dataset validate the effectiveness of the proposed approach.

Anomaly Detection Methods in Autonomous Robotic Missions

“… Methods to find anomalies in spatial, temporal and Spatio-temporal elements. Where S.1 [ 33 ], S.2 [ 34 ], S.3 [ 61 ], S.4 [ 35 ], S.5 [ 62 ], S.6 [ 26 ] represent spatial anomaly detection methods, ST.1 [ 42 ], ST.2 [ 43 ], ST.3 [ 44 ], ST.4 [ 46 ], ST.5 [ 47 ], ST.6 [ 45 ] represent Spatio-temporal anomaly detection methods and T.1 [ 38 ], T.2 [ 39 ], T.3 [ 40 ], T.4 [ 41 ] represent Temporal anomaly detection methods. The colour variation represents the year when the method was first used in Robotics for anomaly detection.…”

“…Detecting spatial features is commonly used in the vision systems of robots, as they capture a single observation at a specific moment. For instance, Dang et al [ 34 ] and Tomoya et al [ 33 ] concentrate on spatial features when discussing anomalies without considering the timing of the observed behaviour. Dang et al used drones for surveillance where objects deviating from the normal soil and bushes, such as cars or blankets, were deemed anomalous.…”

Anomaly Detection Methods in Autonomous Robotic Missions

“…Figure 5.Methods to find anomalies in spatial, temporal and Spatio-temporal elements. Where S.1[33], S.2[34], S.3[61], S.4[35], S.5[62], S.6[26] represent spatial anomaly detection methods, ST.1[42], ST.2[43], ST.3[44], ST.4[46], ST.5[47], ST.6[45] represent Spatio-temporal anomaly detection methods and T.1[38], T.2[39], T.3[40], T.4[41] represent Temporal anomaly detection methods. The colour variation represents the year when the method was first used in Robotics for anomaly detection.…”