# A Review of Video Object Detection: Datasets, Metrics and Methods
## 3 Video Object Detection Methods
### Attention-Related
For situations when it was difficult to determine what the object was in the current frame, the global information was utilized to recognize a fuzzy object according to a clear object with a high similarity in another frame. When it was difficult to find out where the object was in a frame, the local localization information was used by taking the difference between adjacent frames if it was moving. More specifically, RPN was used to generate candidate proposals from those local frames (adjacent frames of current frames) and global frames. Then, a relation module was set up to aggregate the features of candidate proposals on global frames into that of local frames. This was named the global aggregation stage. With this method, the global information was integrated into the local frames. Then, features of the current frame were further augmented by the relation modules in the local aggregation stage. In order to expand the aggregation scale, an efficient module (Long Range Memory (LRM)) was designed where all the features computed in the middle were saved and utilized in a following detection. Evaluated on the ImageNet VID dataset, MEGA with ResNet-101 as backbone achieved an accuracy of 82.9% mAP. Compared with the competitor RDN, MEGA produced 1.1% improvement. Replacing ResNet-101 with ResNeXt-101 or with a stronger backbone to extract features, MEGA obtained an accuracy of 84.1% mAP. With the help of post-processing, it achieved 1.6% and 1.3% improvement with ResNet-101 and ResNeXt-101, respectively. The method Progressive Sparse Local Attention (PSLA) was proposed in [76] to make use of the long term temporal information for enhancement on each feature cell in an attention manner. PSLA establishes correspondence by propagating features in a local region with a gradually sparser stride according to the spatial information across frames. Recursive Feature Updating (RFU) and Dense Feature Transforming (DenseFT) were also proposed based on PSLA to model the temporal relationship and enhance the features in a framework shown in Figure 7. More specifically, features were propagated in an attention manner. First, the correspondence between each feature cell in an embedding feature map of a current frame and its surrounding cells was established with a progressive sparser stride from the center to the outside of another embedding feature map of a support frame. Second, correspondence weights were used to compute the aligned feature maps. The feature maps were aggregated with the aligned features. In addition, similar to other video object detectors, the features of key frames were propagated to non-key frames. A lightweight network was then applied to extract low-level features on non-key frames and fuse them with the features propagated from key frames (DenseFT). Feature propagation was also employed between key frames, and key frame features were updated recursively by an update network (RFU). Hence, features were enriched by the temporal information with DenseFT and RFU, which were further used for detection. Based on the experimentations done in [76], an accuracy of 81.4% mAP was achieved on the ImageNet VID dataset.