## 1 Introduction
### Related Work  
One of the state-of-the-art methods for ROI detection is the Gaussian Mixture Model (GMM) [19; 20; 21], in which fore-background are modeled with some weighted mixture components. Another typical method is named as sparse and low-rank decomposition [11], in which moving objects are computed as the sparse matrix. Derived from this idea, principle component analysis (PCA) [22] is used to efficiently exploit the low-rank structure within errors or outliers, and it is well extended by RPCA [23; 24; 25] where the robustness is improved to work even in the presence of large errors. Also, a probabilistic model for RPCA is proposed in [26] which provides a good visual results. Other low-rank models are treated as the variations of RPCA such as Grouse [27], RASL [28] and SSGodec [29], in which the computing costs are optimized. Moreover, texture-based objects extraction is proposed in [PERSON] et al. [30; 31; 32; 33]. In these works, the authors cluster and extract dynamic textures by the HEM-DTM approach corresponding with Hidden Markov Model (HMM). In addition, background modeling-based approaches such as Vibe [34], LBP [35], SOBS [36], etc. have introduced different background-model update policies based on time, color-distance, and some reliable factors. Meanwhile, the machine learning approaches such as unsupervised clustering [37; 38], Markov Random Field (MRF) for graph cut [39; 40; 41; 42] also achieve a good performance on ROI extraction and detection.  
The other parts of research for ROI detection methods focus on deep-learning based method. Architectures of deep neural network with repeated convolution and pooling layers have become the dominant idea for high-quality objects recognition and detection [43]. [PERSON] et al. [44] introduced the idea of bounding box masks regression for object detection. Similar works on MultiBox [45; 46], improved the previous work by enhancing efficiency and scalability. [PERSON] et al. [47] improved the architecture of R-CNN by proposing a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network. [PERSON] et al. [48] designed a framework of Spatial Pyramid Pooling (SPP) which enhances robustness to size variations in the network. The paper also claimed that the detection speed was improved compared to R-CNN. YOLO by [PERSON] et al. [49] used a single network to predict bounding boxes and class labels for objects. Hence, the detection speed has a significant breakthrough. Later, Single Shot Multibox Detector (SSD) [50] by [PERSON] et al. discretized the output space of bounding boxes into a set of default boxes over different resolutions per feature map location. Both the detection speed and the accuracy have been improved since it only uses a single deep neural network with multiple resolutions of feature maps for boxes regression. Other R-CNN based methods [51; 52; 53] attempted to optimize the network structure by introducing dense connections, reusing computations, applying per-region subnetwork structures to improve the detection performance. However, the multi-stage pipeline training framework of R-CNN brings expensive training costs in space and time. Meanwhile, features extracted from each object proposal are slow at test time [54]. As for machine learning-based methods, a state-of-the-art motivation is to detect objects by using pattern mining with a dictionary model [55]. Our previous work [56] proposed a word-topic model based on Latent Dirichlet Allocation (LDA) model, in which the image features are transformed into corresponding words for ROI object description. Also, an Anchor-box fine-tuning algorithm is proposed to improve the detection precision. It makes a trade-off between the computing costs and model accuracy.