Sparse reconstruction cost for abnormal event detection

By Yang Cong, Junsong Yuan, Ji Liu. 2011

843 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Epidemiology

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Citations in the corpus

C3D: Generic Features for Video Analysis (2014, background): “The computer vision community has been working on video analysis for decades and tackled different problems such as action recognition [2, 31, 40], abnormal event detection [3, 7, 55], and activity understanding [29].”
Abnormal event detection in crowded scenes using sparse representation (2013, methodology): “In this paper, we propose a new criterion, sparse reconstruction cost (SRC), for abnormal event detection in the crowded scene.”
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework (2017, methodology): “[29][17][5] use the reconstruction error to determine whether a frame is abnormal or not.”
HRN: A Holistic Approach to One Class Learning (2020, background): “Recently, image and video based applications have also become popular [13, 49, 70].”
Active Lifelong Learning With "Watchdog" (2018, methodology): “Motivated by (Cong, Yuan, and Liu 2011; 2013), whether wt+1 is unknown or not is determined by the linear reconstruction cost, defined as: S(wt+1) = 1 2 ‖wt+1 −Dr∗‖22 , s.t. : ‖r∗‖0 ≤ μ1, (7) where r∗ is the optimal sparse coding coefficients, μ ≥ 0 controls the sparsity of r∗, and S(wt+1) can…”
Detecting anomalous events in videos by learning deep representations of appearance and motion (2017, methodology): “False Positive Rate (FPR) Tr ue P os iti ve R at e (T PR ) MDT[16] MPPCA[10] Social Force + MPPCA[16] Social Force[17] Sparse Reconstruction[4] Detection at 150FPS[15] AMDN”
Future Frame Prediction for Anomaly Detection - A New Baseline (2017, methodology): “Further, based on the features used, all existing methods can be roughly categorized into two categories: i) hand-crafted features based methods [6][20].”
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder (2017, background): “A popular approach adopted by researchers in this area is to ﬁrst learn the normal patterns from the training videos, then anomalies are detected as events deviated from the normal patterns [13, 2, 32, 12].”
Generative Probabilistic Novelty Detection with Adversarial Autoencoders (2018, methodology): “crementalKernel Null Space Based Discriminant Analysis (IKNDA). Sinceoutliersdonothavesparserepresentations,self-representationapproacheshavebeenproposed for outlier detection in a union of subspaces [4, 25]. Similarly, deep learning based approaches have used neural networks and leveraged the recon”
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (2020, methodology): “Without being able to employ standard supervision, researchers have proposed alternative approaches ranging from distance-based [17, 19, 37, 38, 40, 44, 45, 46, 47, 50, 52, 59] and reconstruction-based strategies [5, 13, 14, 27, 29, 31, 34, 36, 41, 51, 53] to probabilistic [1, 2, 4, 12, 16, 21, 32, ”
Abnormal event detection in videos using generative adversarial nets (2017, methodology): “However, despite a lot of research has been done in this area in the past years [1, 2, 3, 4, 5, 6, 7], the problem is still open.”
Unmasking the Abnormal Events in Video (2017, methodology): “We perform abnormal event detection experiments on the Avenue [13], the Subway [1], the UCSD [14] and the UMN [15] data sets in order to compare our unsupervised approach with a state-of-the-art unsupervised method [6] as well as several supervised methods [5, 9, 13, 14, 15, 16, 18, 20, 24, 25].”
Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis (2015, background): “[22] introduced a sparse reconstruction cost (SRC) over the normal dictionary to measure the normality, and use it to detect local and global abnormalities in a scene.”
Learning Memory-Guided Normality for Anomaly Detection (2020, methodology): “Reconstructive models encode normal patterns using representation learning methods such as an AE [48, 36], a sparse dictionary learning [6, 49, 24], and a generative model [43].”
Latent Space Autoregression for Novelty Detection (2018, background): “Therefore, prior works tackle such problem by following principles derived from the unsupervised learning paradigm [9, 34, 11, 23, 27].”
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence (2019, background): “Such sparsity-based methods have achieved state-of-the-art performances [6, 55].”
Abnormal Event Detection at 150 FPS in MATLAB (2013, methodology): “Algorithm 1 Training for Sparse Combination Learning Input: X , current training features Xc = X initialize S = ∅ and i = 1 repeat repeat Optimize {Si,β} with Eqs.”
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015, methodology): “…MPPCA[10]Social Force + MPPCA[16] Social Force[17] Sparse reconstruction[4] Local statistical[22] Detection at 150FPS[15] AMDN MDT[16]MPPCA[10]Social Force + MPPCA[16] Social Force[17] Sparse Reconstruction[4] Detection at 150FPS[15] The Train dataset [36] depicts moving people inside a train.”
Learning Temporal Regularity in Video Sequences (2016, background): “A state-of-the-art approach for such un-supervised modeling involves a combination of sparse coding and bag-of-words [8–10].”