以多幀預測模型進行影片異常偵測

Abstract

影片異常偵測 (Video anomaly detection) 在影片認知中是一項越來越重要且有發展性的任務,它的目標為自動偵測一段影片中很少發生或非預期的事件。先前研究使用的方法多為非監督式學習,從訓練資料中習得正常影片的特徵表示,異常定義為測試資料遠離正常特徵分佈的事件。在深度學習方面,多數的方法為訓練一自動編碼器 (Autoencoder) 並減少正常影片中的重構錯誤(reconstructions error),測試資料若為正常則可以很好的重構,然而,異常 事件不一定會導致大的重構錯誤。為了解決這個問題,我們提出了一個多幀預測模型 (multi-frame prediction framework),它可以透過放大非預期的改變來改善自動編碼器的缺點,我們用卷積式長短期記憶神經網路(ConvLSTM)做為主要的架構。對於移動路線以及外表變化相關的異常事件,透過實驗結果我們驗證了在多幀模型中用後面預測的幀來計算異常分數的可效性。此外,我們從 YouTube 蒐集了一個新的交通事故資料集,這個資料集包含了不同種類的事故以及多樣的環境,和現有的異常偵測資料集相比,更貼近現實應用、也更加有挑戰性。我們的方法和先前表現最好的模型相比,在物體外表簡單且異常事件為路徑及外表變化相關的情況下表現會提升。

Video anomaly detection which intents to identify rarely-happened or unexpected events is a worthy and developmental problem in video understanding tasks. Most of the previous works deal with the problem in an unsupervised way by learning normal representations of training data and identified the outliers as anomalies. Common deep learning-based methods are reconstruction-based. They train an autoencoder by minimizing the reconstruction errors of regular videos. Nevertheless, abnormal events don't always lead to larger reconstruction errors. To address this issue, We propose using multi-frame prediction framework to enlarge the unexpected change and overcome the generalization property which stems from the use of an autoencoder. We use ConvLSTM model as the multi-frame predictor and show the effectivenes of utilizing latter frames for computing the frame anomaly scores. Experimental results show that our model leads to better performance on motion and appearance deformation irregularities. In addition, we collect a new car crash dataset which contains various car accidents as abnormal events from YouTube for evaluation. Compared to existing anomaly detection datasets, it is a more challenging and practical dataset due to the diversity of events and its different environmental conditions. Our model achieves comparable results in popular existing anomaly detection datasets and outperforms the state-of-the-art on the new proposed dataset.