# Local Attention Sequence Model for Video Object Detection
## 1 Introduction  
Object detection is a fundamental problem in computer vision and has been widely used in the fields of surveillance, robots, medical intelligence, etc. In recent years, with the rapid popularization of deep convolutional networks, many scholars have also conducted much research on object detection algorithms based on deep convolutional networks [1, 2], which has greatly improved the performance of image object detection. In 2013, [PERSON] proposed R-CNN [3], which used a deep convolutional network to achieve object detection for the first time, and the mAP index on the VOC dataset was approximately doubled when compared to traditional detection methods. Subsequently, [PERSON] proposed Faster R-CNN [4] and designed a region proposal network to extract candidate regions, which improves the detection accuracy and greatly reduces the detection running time. However, the speed of the two-stage detection model based on R-CNN is low. [PERSON] proposed the YOLO [5] detection framework in 2015, which rasterizes images and predicts the object category and bounding box for each grid at the same time.  
Applying such image-based object detectors to the domain of videos, however, is often unsatisfactory due to the deteriorated appearance caused by issues such as motion blur, out-of-focus camera, and rare poses frequently encountered in videos. These problems cannot be effectively solved by relying only on static images. The video can provide context and temporal information containing multiple frames of images. Combining this information can solve the above problems more effectively. Existing methods that leverage temporal information for object detection from videos usually use optical flow to propagate high-level features across frames. Extra optical flow models, e.g., FlowNet [6, 7], have to be utilized to establish motion information and achieve better performance, which leads to excessive model parameters and calculations that are not conducive to model deployment.  
In addition, the optical flow models establish motion information between local pixels, and it is difficult to model the continuity between high-level semantic features.  
Instead of relying on optical flow, we propose an innovative video object detection model based on local attention. Specifically, we design the spatial attention module and local attention sequence model to improve video object detection accuracy and modify ConvGRU (convolutional gated recurrent units) [8] to process video context and temporal information in order to improve object detection reliability.  
We conducted extensive experiments on ImageNet VID for video object detection. Our results outperform the original method in accuracy and achieve real-time detection in the same time duration.  
In summary, our contributions are as follows:  
We introduce a novel video object detector based on local attention to establish the spatial and temporal correspondence across frames without extra optical flow models.  
We propose a spatial attention module and local attention sequence module and modify ConvGRU to model spatial and temporal appearance and enhance feature representation. We conduct experiments on ImageNet VID and achieve improved performance.