Abstract. Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available 1 . In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version 2 . DetectionIn data association based MOT, the tracking performance is heavily affected by the detection results. We implement our detector based on Faster R-CNN [14]. In our implementation, the CNN model is fine-tuned from the VGG-16 on ImageNet. In considering the definition of MOTA in MOT16 [12], the sum of false negatives (FN) and false positives (FP) poses a large impact on the value of MOTA. In Table 1, we show that our detection optimization strategies lead to the significant decrease in the sum of FP and FN 3 .1 https://drive.google.com/open?id=0B5ACiy41McAHMjczS2p0dFg3emM 2 We use POI to denote our online tracker and KDNT to denote our offline tracker in submission. 3 We use detection score threshold 0.3 for Faster R-CNN and -1 for DPMv5 , labeling the ID of detection box with incremental integer, and evaluate FP and FN with MOT16 devkit.

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This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture. Instead of using only two independent points, we design a multi-point supervision formulation to encode more clues in order to reduce the impact of inaccurate prediction of specific points. To take the full advantage of the correlation of points in a grid, we propose a two-stage information fusion strategy to fuse feature maps of neighbor grid points. The grid guided localization approach is easy to be extended to different state-of-the-art detection frameworks. Grid R-CNN leads to high quality object localization, and experiments demonstrate that it achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.

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