Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space. (2) The fixed number of prototypes from subjective assumptions ignores the data feature differences and diversity. To overcome these drawbacks, we introduce DLAN-AC, a Dynamic Local Aggregation Network with Adaptive Clusterer, for anomaly detection. First, The proposed DLAN can automatically learn and aggregate high-level features from the AE to obtain more representative prototypes, while freeing up extra memory space. Second, The proposed AC can adaptively cluster video data to derive initial prototypes with prior information. In addition, we also propose a dynamic redundant clustering strategy (DRCS) to enable DLAN for automatically eliminating feature clusters that do not contribute to the construction of prototypes. Extensive experiments on benchmarks demonstrate that DLAN-AC outperforms most existing methods, validating the effectiveness of our method. Our code is publicly available at https://github.com/Beyond-Zw/DLAN-AC.
翻译:基于内存增强自动编码器(AE)的现有异常探测方法有以下缺点:(1) 建立记忆库需要额外的记忆空间。(2) 主观假设的固定原型数忽略了数据特征的差异和多样性。为克服这些缺陷,我们引入了DLAN-AC(一个具有适应性封条器的动态本地聚合网络),以探测异常现象。首先,拟议的DLAN可以自动从AE学习和汇总高层次的特征,以获得更具代表性的原型,同时释放额外的记忆空间。第二,拟议的AC可以调整性地组合视频数据,以利用先前的信息生成初始原型。此外,我们还提议了一个动态冗余集战略(DRCS),以使DLAN-AC能够自动消除无助于原型构造的特性集群。关于基准的广泛实验表明,DLAN-AC超越了大多数现有方法,验证了我们的方法的有效性。我们的代码可在https://github.com/Beyonnd-Zw/DLAN-AC上公开查阅。