# A Review of Video Object Detection: Datasets, Metrics and Methods
## 4 Comparison of Video Object Detection Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Framework** & **Backbone** & **mAP (\%)** & **Runtime (fps)** \\ \hline Single frame & R-RCN [19] & ResNet-101 & 73.9 & 4.05 K \\  & Impression network [68] & Modified ResNet-101 & 75.5 & 20 1060 \\  & FGFA [22] & ResNet-101 & 76.3 & 1.36 K \\ Flow based & DFF [28] & ResNet-101 & 73.1 & 20.25 K \\  & THP [69] & ResNet-101 + DCN & 78.6 & 13.0X/8.6K \\  & THPM [27] & Mobilenet & 60.2 & 25.6 HuaiWei Mate\$ \\  & Looking fast and slow [70] & Interleaved & 61.4 & 23.5 Pixel 3 phone \\ LSTM based & LSTM-SSD [71] & MobilenetV2-SSDLite & 53.5 & – \\  & Flow\&LSTM [72] & ResNet-101 & 75.5 & – \\  & OGEMN [74] & ResNet-101 & 79.3 & 8.9 (1080 Ti) \\  & OGEMN [74] & ResNet-101 + DCN & 80.0 & – \\  & ResNeXt-101 & 77.1 & 30.8V\(\backslash\)18.73X \\  & PSLA [76] & ResNet-101 + DCN & 80.0 & 26.0V\(\backslash\)13.34X \\ Attention based & SELSA [77] & ResNeXt-101 & 80.25 & – \\  & RDN [75] & ResNet-101 & 81.8 & 10.6 V100 \\  & ResNeXt-101 & 83.2 & – \\  & MEGA [25] & ResNet-101 & 82.9 & 8.73 2080 Ti \\ Tracking based & D\&T loss [81] & ResNet-101 & 75.8 & 7.8X \\ Track assisted [26] & ResNet-101 & 70.0 & 30 XP \\ Others & TCNN [24] & GoogLeNet & 73.8 & – \\  & STSN [83] & ResNet-101 + DCN & 78.9 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison among the video object detection methods without post processing; note that the runtime is based on the NVIDIA GPU used in the references: K means K40, XP means Titan XP, X means Titan X, V means Titan V, 1060 means GeForce GTX 1060, 1080 Ti means GeForce GTX 1080 Ti, 2080 Ti means GeForce GTX 2080 Ti. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Framework** & **Backbone** & **mAP (\%)** & **Runtime (fps)** \\ \hline Flow-based & FGFA [22] & ResNet-101 & 78.4 & – \\  & Looking fast and slow [70] & Interleaved + Quantization + Async & 59.3 & 72.3 Pixel 3 phone \\  & MobilenetV2-SSDLite + LSTM (\(\alpha=1.4\)) [71] & MobilenetV2-SSDLite & 64.1 & 4.1 Pixel 3 phone \\  & MobilenetV2-SSDLite + LSTM (\(\alpha=1.0\)) [71] & MobilenetV2-SSDLite & 59.1 & – \\  & MobilenetV2-SSDLite + LSTM (\(\alpha=0.5\)) [71] & MobilenetV2-SSDLite & 50.3 & – \\  & MobilenetV2-SSDLite + LSTM (\(\alpha=0.35\)) [71] & MobilenetV2-SSDLite & 45.1 & 14.6 Pixel 3 phone \\  & OGEMN [74] & ResNet-101 & 80.8 & – \\  & ResNeXt-101 & 81.6 & – \\  & FSLA [76] & ResNet-101 & 78.6 & 5.7X \\ Attention-based & SELSA [77] & ResNet-101 + DCN & 81.4 &  
time. Because of only using the features within the proposals, the performance relies on the effect of RPN to a certain extent. Here, it is rather difficult to utilize more comprehensive information. In the tracking-based group, the methods are assisted by tracking. D&T loss [81] achieved 75.8% mAP. Tracking is an efficient method to employ the temporal information with a detector assisted by a tracker. However, it cannot solve the problems created by motion blur and video defocus directly. As the detection performance relies on the tracking performance, the detector part suffers from tracking errors. There are also other standalone methods including TCNN [24], STSN [83] and STMN [87]. In order to further improve the performance in terms of detection accuracy, post-processing can be added to the above methods. The results with post-processing are shown in Table 5. One can easily see that with post-processing, the accuracy is noticeably improved. For example, the accuracy of MEGA is improved from 84.1% to 85.4% mAP.