@article{ramachandra_jones_vatsavai_2022, title={A Survey of Single-Scene Video Anomaly Detection}, volume={44}, ISSN={["1939-3539"]}, DOI={10.1109/TPAMI.2020.3040591}, abstractNote={This article summarizes research trends on the topic of anomaly detection in video feeds of a single scene. We discuss the various problem formulations, publicly available datasets and evaluation criteria. We categorize and situate past research into an intuitive taxonomy and provide a comprehensive comparison of the accuracy of many algorithms on standard test sets. Finally, we also provide best practices and suggest some possible directions for future research.}, number={5}, journal={IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE}, author={Ramachandra, Bharathkumar and Jones, Michael J. and Vatsavai, Ranga Raju}, year={2022}, month={May}, pages={2293–2312} }
 @article{chen_dutton_ramachandra_wu_vatsavai_2021, title={Local Clustering with Mean Teacher for Semi-supervised learning}, ISSN={["1051-4651"]}, DOI={10.1109/ICPR48806.2021.9412469}, abstractNote={The Mean Teacher (MT) model of Tarvainen and Valpola has shown good performance on several semi-supervised benchmark datasets. MT maintains a teacher model's weights as the exponential moving average of a student model's weights and minimizes the divergence between their probability predictions under diverse perturbations of the inputs. However, MT is known to suffer from confirmation bias, that is, reinforcing incorrect teacher model predictions. In this work, we propose a simple yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias. In MT, each data point is considered independent of other points during training; however, data points are likely to be close to each other in feature space if they share similar features. Motivated by this, we cluster data points locally by minimizing the pairwise distance between neighboring data points in feature space. Combined with a standard classification cross-entropy objective on labeled data points, the misclassified unlabeled data points are pulled towards high-density regions of their correct class with the help of their neighbors, thus improving model performance. We demonstrate on semi-supervised benchmark datasets SVHN and CIFAR-10 that adding our LC loss to MT yields significant improvements compared to MT and performance comparable to the state of the art in semi-supervised learning11The code is available at: https://github.com/jay1204/local_clustering_with_mt_for_ssl.}, journal={2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)}, author={Chen, Zexi and Dutton, Benjamin and Ramachandra, Bharathkumar and Wu, Tianfu and Vatsavai, Ranga Raju}, year={2021}, pages={6243–6250} }
 @article{ramachandra_jones_vatsavai_2021, title={Perceptual metric learning for video anomaly detection}, volume={32}, ISSN={["1432-1769"]}, DOI={10.1007/s00138-021-01187-5}, number={3}, journal={MACHINE VISION AND APPLICATIONS}, author={Ramachandra, Bharathkumar and Jones, Michael and Vatsavai, Ranga Raju}, year={2021}, month={May} }
 @article{ramachandra_dutton_vatsavai_2019, title={Anomalous cluster detection in spatiotemporal meteorological fields}, volume={12}, ISSN={["1932-1872"]}, DOI={10.1002/sam.11398}, abstractNote={Finding anomalous regions in spatiotemporal climate data is an important problem with a need for greater accuracy. The collective and contextual nature of anomalies (e.g., heat waves) coupled with the real‐valued, seasonal, multimodal, highly correlated, and gridded nature of climate variable observations poses a multitude of challenges. Existing anomaly detection methods have limitations in the specific setting of real‐valued areal spatiotemporal data. In this paper, we develop a method for extreme event detection in meteorological datasets that follows from well known distribution‐based anomaly detection approaches. The method models spatial and temporal correlations explicitly through a piecewise parametric assumption and generalizes the Mahalanobis distance across distributions of different dimensionalities. The result is an effective method to mine contiguous spatiotemporal anomalous regions from meteorological fields which improves upon the current standard approach in climatology. The proposed method has been evaluated on a real global surface temperature dataset and validated using historical records of extreme events.}, number={2}, journal={STATISTICAL ANALYSIS AND DATA MINING}, author={Ramachandra, Bharathkumar and Dutton, Benjamin and Vatsavai, Ranga Raju}, year={2019}, month={Apr}, pages={88–100} }
 @article{ramachandra_nawathe_monroe_han_ham_vatsavai_2018, title={Real-Time Energy Audit of Built Environments: Simultaneous Localization and Thermal Mapping}, volume={24}, ISSN={["1943-555X"]}, DOI={10.1061/(ASCE)IS.1943-555X.0000431}, abstractNote={AbstractLeveraging thermography for managing built environments has become prevalent as a robust tool for detecting, analyzing, and reporting their performance in a nondestructive manner. Despite m...}, number={3}, journal={JOURNAL OF INFRASTRUCTURE SYSTEMS}, author={Ramachandra, Bharathkumar and Nawathe, Pranav and Monroe, Jacob and Han, Kevin and Ham, Youngjib and Vatsavai, Ranga Raju}, year={2018}, month={Sep} }
 @inproceedings{chen_ramachandra_vatsavai_2017, title={Hierarchical change detection framework for biomass monitoring}, DOI={10.1109/igarss.2017.8127030}, abstractNote={In this paper, we present a nearest neighbor based hierarchical change detection methodology for analyzing multi-temporal remote sensing imagery. A key contribution of this work is to define change as hierarchical rather than boolean. Based on this definition of change pattern, we developed a novel time series similarity based change detection framework for identifying inter-annual changes by exploiting phenological properties of growing crops from satellite time series imagery. The proposed framework consists of four components: hierarchical clustering tree construction, nearest neighbor based classification, relaxation labeling, and change detection using similarity hierarchy. Though the proposed approach is unsupervised, we present evaluation using manually induced change regions embedded in the real dataset. We compare our method with the widely used K-Means clustering and evaluation shows that K-Means over-detects changes in comparison to our proposed method.}, booktitle={2017 ieee international geoscience and remote sensing symposium (igarss)}, author={Chen, Z. and Ramachandra, B. and Vatsavai, Ranga Raju}, year={2017}, pages={620–623} }
 @inproceedings{chen_vatsavai_ramachandra_zhang_singh_sukumar_2016, title={Scalable nearest neighbor based hierarchical change detection framework for crop monitoring}, DOI={10.1109/bigdata.2016.7840735}, abstractNote={Monitoring biomass over large geographic regions for changes in vegetation and cropping patterns is important for many applications. Changes in vegetation happen due to reasons ranging from climate change and damages to new government policies and regulations. Remote sensing imagery (multi-spectral and multi-temporal) is widely used in change pattern mapping studies. Existing bi-temporal change detection techniques are better suited for multi-spectral images and time series based techniques are more suited for analyzing multi-temporal images. A key contribution of this work is to define change as hierarchical rather than boolean. Based on this definition of change pattern, we developed a novel time series similarity based change detection framework for identifying inter-annual changes by exploiting phenological properties of growing crops from satellite time series imagery. The proposed framework consists of three components: hierarchical clustering tree construction, nearest neighbor based classification, and change detection using similarity hierarchy. Though the proposed approach is unsupervised, we present evaluation using manually induced change regions embedded in the real dataset. We compare our method with the widely used K-Means clustering and evaluation shows that K-Means over-detects changes in comparison to our proposed method.}, booktitle={2016 IEEE International Conference on Big Data (Big Data)}, author={Chen, Z. X. and Vatsavai, Ranga Raju and Ramachandra, B. and Zhang, Q. and Singh, N. and Sukumar, S.}, year={2016}, pages={1309–1314} }