# FPGA-Based Vehicle Detection and Tracking Accelerator

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## Abstract

**:**

## 1. Introduction

- We trained the YOLOv3 and YOLOv3-tiny networks using the UA-DETRAC dataset [17]. Then, we incorporate the dynamic threshold structured pruning strategy based on binary search and the dynamic INT16 fixed-point quantization algorithm to compress the model.
- A reidentification dataset was generated based on the UA-DETRAC dataset and used to train the appearance feature extraction network of the Deepsort algorithm with a modified input size to improve the vehicle tracking performance.
- We designed and implemented a vehicle detector based on an FPGA using high level synthesis (HLS) technology. At the hardware level, optimization techniques such as the Im2col+GEMM and Winograd algorithms, parameter rearrangement, and multichannel transmission are adopted to improve the computational throughput and balance the resource occupancy and power consumption. Compared with the other related work, vehicle detection performance with higher precision and higher throughput is realized with lower power consumption.
- Our design adopts a loosely coupled architecture, which can flexibly switch between the two detection models by changing the memory management module, optimizing the balance between the software flexibility and high computing efficiency of the dedicated chips.

## 2. Background and Related Work

#### 2.1. YOLO

#### 2.2. Deepsort

#### 2.3. Simplification of the DNN

#### 2.4. CNN Accelerator Based on an FPGA

## 3. Optimization and Implementation of the Vehicle Detector

#### 3.1. Model Compression

#### 3.1.1. Structured Pruning Based on Dynamic Threshold of Binary Search

#### 3.1.2. Dynamic 16-bit Fixed-Point Quantization

#### 3.2. Self-Generated REID-UADETRAC Dataset

#### 3.3. Overview of the Accelerator Architecture

#### 3.4. Strategies of Memory Optimization

#### 3.4.1. Model Configurability and Memory Interlayer Multiplexing

#### 3.4.2. Parameter Rearrangement in Memory

#### 3.4.3. Multichannel Transmission

#### 3.4.4. Multi-Level Pipeline Optimization

#### 3.5. Strategies of Computational Optimization

#### Multiscale Convolution Acceleration Engines

**Im2col+GEMM:**The Im2col+GEMM algorithm reduces the time complexity of the convolution operation from $O\left({n}^{6}\right)$ to $O\left({n}^{3}\right)$ by using the matrix multiplication instead of the convolution, as shown below:

**Winograd convolution:**

#### 3.6. Max-Pooling and Upsampling Parallel Optimization

#### Fused Convolution and Batch Normalization Computation

## 4. Experiments

#### 4.1. Experimental Setup

#### 4.2. Dataset and Model Training

#### 4.3. RE-ID Deepsort

#### 4.4. Comparison and Discussion

#### 4.5. Scalability Discussion

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Strategies for sparse regularized channel pruning. (

**a**) Structure before pruning. (

**b**) Structure after pruning.

**Figure 18.**Tracking using RE-ID Deepsort. (

**a**) Detection result of MVI_40701. (

**b**) Detection result of MVI_40771. (

**c**) Detection result of MVI_40863.

Variables | Meaning |
---|---|

$\lambda $ | The parameter for regulating the effect of the spatial Mahalanobis distance and visual distance on the cost function. |

${y}_{i}$ | The state vector of the i-th prediction frame. |

${S}_{i}$ | The covariance matrix of the average tracking results between the detection frame and track i. |

${d}_{j}$ | Detection box j. |

${r}_{i}$ | The appearance descriptor extracted from detection box j. |

${R}_{i}$ | The last 100 appearance descriptor sets associated with track i. |

Symbol | Meaning |
---|---|

I | The input feature map. |

W | The weights of the convolution layer. |

B | The bias of the convolution layer. |

O | The output feature map. |

$IH$ | The height of the input feature map. |

$IW$ | The width of the input feature map. |

$IC$ | The number of input channels. |

K | The kernel size. |

$OH$ | The height of the output feature map. |

$OW$ | The width of the output feature map. |

$OC$ | The number of output channels. |

$pad$ | The padding. |

S | The stride. |

$Tx$ | Parallelism of multiply-add operations on input feature maps. |

$Ty$ | Parallelism of multiply-add operations on output feature maps. |

Symbol | Meaning |
---|---|

${O}_{norm}$ | The output of the feature map after batch normalization. |

$\gamma $ | The parameter that controls the variance of ${O}_{norm}$. |

${\sigma}^{2}$ | The variance of O. |

$\u03f5$ | A small constant used to prevent numerical error. |

O | The output of the feature map. |

$\mu $ | The estimate of the mean of O. |

$\beta $ | The parameters that control the mean of ${O}_{norm}$. |

Model | Pruning Rate | [email protected] | Model Size (MB) | Parameters ($\times {10}^{3}$) | BFLOPs |
---|---|---|---|---|---|

YOLOv3 | 0 | 0.671 | 235.06 | 61523 | 65.864 |

YOLOv3 | 85% | 0.711 | 33.55 | 8719 | 19.494 |

YOLOv3-tiny | 0 | 0.625 | 33.10 | 8670 | 5.444 |

YOLOv3-tiny | 85% | 0.625 | 1.02 | 267 | 1.402 |

YOLOv3-tiny | 85% + 30% | 0.599 | 0.59 | 69 | 0.735 |

Model | Number of Vehicles Detected |
---|---|

YOLOv3 | 27 |

YOLOv3-prune85% | 27 |

YOLOv3-tiny | 24 |

YOLOv3-tiny-prune85% | 24 |

YOLOv3-tiny-prune85%+30% | 26 |

Model | Video Stream | IDF1↑ | IDP↑ | IDR↑ | FP↓ | FN↓ | IDs↓ | MOTA↑ | MOTP↓ |
---|---|---|---|---|---|---|---|---|---|

Deepsort | MVI_40701 | 76.4% | 82.5% | 71.1% | 1515 | 3706 | 53 | 66.8% | 0.118 |

RE-ID Deepsort | 79.6% | 86.4% | 76.2% | 1452 | 3686 | 27 | 67.5% | 0.117 | |

Deepsort | MVI_40771 | 69.3% | 74.5% | 66.2% | 2409 | 2409 | 49 | 65.2% | 0.153 |

RE-ID Deepsort | 80.6% | 87.2% | 75.0% | 1015 | 2348 | 13 | 69.6% | 0.155 | |

Deepsort | MVI_40863 | 55.2% | 80.6% | 42.0% | 2076 | 17746 | 51 | 39.2% | 0.138 |

RE-ID Deepsort | 56.1% | 82.0% | 42.6% | 2037 | 17382 | 35 | 40.5% | 0.138 |

Item | Platform | CNN Model | Operation (GOP) | Throughput (fps) | Full Power (W) | Efficiency (GOPS/W) | Cost Efficiency (GOPS/$$\times {10}^{2}$) |
---|---|---|---|---|---|---|---|

Baseline1 | CPU AMD R75800H | YOLOv3-tiny | 0.735 | 10.01 | 45 | 0.16 | 1.96 |

Baseline2 | GeForce RTX 2060 | YOLOv3-tiny | 0.735 | 112.87 | 160 | 0.52 | 16.58 |

Baseline3 | XCZU9EG-FFVB1156 | yolov3-adas-pruned-0.9 | 5.5 | 84.1 | - | 3.71 | 4.16 |

Ref [45] | Nvidia Jetson Nano | YOLOv3-tiny | 1.81 | 17 | 10 | 3.08 | 24.62 |

This work | Zynq-7000 | YOLOv3-tiny | 0.735 | 91.65 | 12.51 | 5.43 | 46.51 |

Item | Ref [14] | Ref [37] | Ref [33] | Ref [46] | This Work | ||||
---|---|---|---|---|---|---|---|---|---|

Basic information introduction | |||||||||

Platform | ZYNQ XC7Z020 | Zedboard | Arria-10GX1150 | Virtex-7: XC7VX690T-2 | Zynq-7000 | ||||

Precision | Fixed-16 | Fixed-16 | Int8 | Float-32 | Float-32 | Fixed-16 | |||

CNN Model | YOLOv2 | YOLOv2 | YOLOv2-tiny | YOLOv2 | YOLOv2-tiny | YOLOv3 | YOLOv3-tiny | YOLOv3 | YOLOv3-tiny |

Dataset | COCO | COCO | VOC | VOC | UA-DETRAC | ||||

Hardware resource consumption | |||||||||

BRAM | 87.5 | 88 | 96% | 1320 | 98.5 (19.7%) | 132.5 (26.5%) | |||

DSPs | 150 | 153 | 6% | 3456 | 301 (33.8%) | 144 (16.2%) | |||

LUTs | 36 576 | 37 342 | 45% | 637 560 | 38 336 (22.3%) | 38 228 (22.2%) | |||

FFs | 43 940 | 35 785 | 717 660 | 62 988 (18.3%) | 42 853 (12.5%) | ||||

Performance comparison | |||||||||

mAP | 0.481 | 0.481 | - | 0.744 | 0.548 | 0.711 | 0.599 | 0.711 | 0.599 |

Operations (GOP) | 29.47 | 29.47 | 5.14 | 4.2 | 1.24 | 19.494 | 0.735 | 19.494 | 0.735 |

Freq (MHz) | 150 | 150 | 204 | 200 | 210 | 230 | |||

Performance (GOP/s) | 64.91 | 30.15 | 21.97 | 182.36 | 389.90 | 41.39 | 43.47 | 63.51 | 67.91 |

Throughput(fps) | 2.20 | 1.02 | 4.27 | 61.90 | 314.2 | 2.12 | 59.14 | 3.23 | 91.65 |

Efficiency comparison | |||||||||

Cost Efficiency (GOPS/$×${10}^{2}$) | 44.45 | 20.65 | 15.05 | 17.49 | 46.75 | 28.35 | 29.77 | 43.50 | 46.51 |

DSP Efficiency (GOPS/DSPs) | 0.433 | 0.197 | 0.144 | 2.004 | 0.113 | 0.138 | 0.144 | 0.441 | 0.472 |

Dynamic Power (W) | 1.4 | 1.2 | 0.83 | - | - | 1.80 | 1.48 | 1.52 | 1.31 |

Full Power (W) | - | - | - | 26 | 21 | 13.29 | 12.92 | 12.73 | 12.51 |

Dynamic Energy Efficiency (GOPS/W) | 46.36 | 25.13 | 26.47 | - | - | 22.99 | 29.37 | 41.78 | 51.84 |

Full Energy Efficiency (GOPS/W) | - | - | - | 7.01 | 18.57 | 3.11 | 3.36 | 4.99 | 5.43 |

Precision | Float-32 | Fixed-16 | ||
---|---|---|---|---|

Platform | Zynq-7000 | |||

Freq (MHz) | 200 | 209 | ||

BRAM | 230.5 | 263 | ||

DSPs | 602 | 294 | ||

LUTs | 89,014 | 91,108 | ||

FFs | 149,259 | 89,148 | ||

CNN Model | YOLOv3 | YOLOv3-tiny | YOLOv3 | YOLOv3-tiny |

Performance (GOP/S) | 78.84 | 82.80 | 115.96 | 124.01 |

Throughput (fps) | 4.04 | 112.66 | 5.95 | 168.72 |

Full Power (W) | 16.72 | 16.06 | 15.64 | 15.18 |

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## Share and Cite

**MDPI and ACS Style**

Zhai, J.; Li, B.; Lv, S.; Zhou, Q.
FPGA-Based Vehicle Detection and Tracking Accelerator. *Sensors* **2023**, *23*, 2208.
https://doi.org/10.3390/s23042208

**AMA Style**

Zhai J, Li B, Lv S, Zhou Q.
FPGA-Based Vehicle Detection and Tracking Accelerator. *Sensors*. 2023; 23(4):2208.
https://doi.org/10.3390/s23042208

**Chicago/Turabian Style**

Zhai, Jiaqi, Bin Li, Shunsen Lv, and Qinglei Zhou.
2023. "FPGA-Based Vehicle Detection and Tracking Accelerator" *Sensors* 23, no. 4: 2208.
https://doi.org/10.3390/s23042208