Recurrent Autoregressive Networks for Online Multi-Object Tracking. Presented By: Ishan Gupta
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1 Recurrent Autoregressive Networks for Online Multi-Object Tracking Presented By: Ishan Gupta
2 Outline Multi Object Tracking Recurrent Autoregressive Networks (RANs) RANs for Online Tracking Other State of the Art approaches Evaluation Metrics Used Empirical Results Suggestions for Improvement Possible Extensions to Follow Up
3 Multi Object Tracking Goals Remembering multiple objects seen in a particular frame across time. Exploring new object inceptions at any given timestamp. Online tracking application i.e No dependence on future measurements.
4 Multi Object Tracking Challenges Associating current measurements with the previous tracked objects. Utilizing features from the past history of the tracklet and the accumulated features. Minimizing the track drift getting accumulated at each timestamp. Occlusion and Truncation variance.
5 Building towards RANs AutoRegressive Models Functional coefficient regression model for non-linear time series. The coefficients for the time lagged observations are not constant. Coefficients are inherently functions applied to the k-dimensional features. The features are also updated with time.
6 Building towards RANs Memory Networks One of the important concepts used in solving visual question answering. Input and Output memory representations which store the learnable embeddings. Attention mechanism which learns the soft probabilities depending on query embedding and memory embedding correlations.
7 Building towards RANs Memory Networks
8 Building towards RANs Dynamic Memory Networks Input Module - Processes the input data into an ordered feature space of vectors. Question Module - Processes the question into a embedding. Episodic Memory Module - Aims to retrieve the information from input feature space. Answer Module - Receives both question and memory output to generate the model s predicted answer.
9 Building towards RANs Dynamic Memory Networks
10 Recurrent Autoregressive Networks A deep generative model for sequential data. Estimate the probability of new detections in an autoregressive fashion. Internal and External Memory Internal memory focused on learning what to retrieve from the external memory. External memory helps in training RNNs by using contextual features temporally. Training RNNs for context remembrance can be substituted with priors. Hence, RAN constantly updates the memory in a temporal sliding window. In prior memory networks, we were training a RNN to perform data reading. No assumption or dependence on the linearity of the sequence.
11 Recurrent Autoregressive Networks Models the conditional probability distribution of next input given all previous inputs. i.e P(xt x1...t-1 ) Parameters of AR Model Weighted sum of previous K templates with added gaussian noise. Gaussian Noise for each dimension. Gaussian Noise with zero mean and diagonal covariance.
12 Recurrent Autoregressive Networks GRUs vs LSTMs GRU operate using a reset and update gate. Reset gate helps in learning how much to forget the previous state. The update gate controls how much to update the previous state. LSTMs use output gate to control the exposure of cell state. GRUs expose the entire cell state to the next layer. LSTM unit has separate input and forget gates while GRU uses a single gate.
13 Recurrent Autoregressive Networks Enabling GRUs in RANs to capture long term trend of sequence. GRU maintains and updates the hidden state at each time stamp. The hidden state is used to compute the AR parameters and variance. AR parameters learnt as linear combination of hidden state. Softmax applied to perform soft attention over the temporal space. Variance scaled exponentially to make sure it is positive. AR parameters learnt as linear combination of hidden state. Softmax applied to perform soft attention over the temporal space. Variance scaled exponentially to make sure it is positive.
14 Recurrent Autoregressive Networks Model Schematic
15 Data Association with RANs Problem: Assign measurements at each time step to previously tracked objects. Remember the lost objects for a certain time frame. Start developing trajectories for newly found objects. Using appearance information and motion dynamics as features. Learn specific RANs for each feature. Learning associativity between detection (i) and previous tracked history of an object (l)
16 Data Association with RANs Use different internal and external memories for all features. After associating the new detection with a tracked object, update: The external memory for the object. The hidden state for the corresponding object s RAN. When the target is lost, only update the location dynamics with the mean. Unassociated measurements are initialized as new detections. Terminate the trajectory if it is lost for more than T time steps. Measurements in this approach are the outputs of an object detector.
17 Algorithm
18 Training RANs The main goal is to discriminate between ground truth and false associations. Maximize the likelihood of the conditional probability for feature association. Sample locations from the boxes having an IOU > 0.5 with the ground truth. Using the ground truth as it is, will make the learning the detector specific.
19 Training Details Faster-RCNN for object detections. 128 dimensional internal memories for appearance. 32 dimensional internal memories for motion. Time span for external memories kept as 10. Appearance Features: Motion Features: 256 Dimensional FC-8 representation from Inception. 4 Dimensional vector (Relative center coordinates, width and height) Adam Optimizer with learning rate as (1e-3). RnnDrop to prevent overfitting.
20 Other State of the Art Approaches Formulating the problem as decision making in Markov Decision Processes. Learning State Transitions corresponding to Birth/Death Processes. Learning policies for data association.
21 Other State of the Art Approaches
22 Evaluation Metrics Multiple Object Tracking Accuracy Multiple Object Tracking Precision ID Switches Percentage of Mostly Tracked Targets Percentage of Mostly Lost Targets Total False Positives Total False Negatives.
23 Evaluation Metrics Design Rules: Should be able to judge tracker s precision in object localization. Shouble be able to produce exactly one trajectory per object. Tracking Precision: How well exact the position of objects are estimated. Tracking Accuracy: How many mistakes the tracker made in terms of misses, false positives, mismatches and failure to recover tracks.
24 Evaluation Metrics Valid Correspondences and Mismatches.
25 Evaluation Metrics mt = Number of misses fpt = Number of false positives mmet = Number of mismatches gt = Ground Truth Objects Dt = Distance between matched correspondences Ct = Number of matched object-hypothesis pairs
26 Results
27 Experiments AM(A+M) GRU AVE TIV RAN (using only appearance) (using only motion dynamics) (both appearance and motion) (Using only GRU) (Averaging features directly) (Time Invariant Parameters) (GRU + External Memory)
28 Experiments Effect of window size of external memory.
29 Experiments How External Memories help during occlusion?
30 Conclusion Multi Object Tracking can be solved in an autoregressive fashion. The features at current time can be represented as a gaussian distribution centered around the linear combination of previous features. GRUs start learning from previous contexts when the object starts getting occluded. Combining Internal and External memories help in learning this sequential data problem.
31 References Fang, Kuan, Yu Xiang, and Silvio Savarese. "Recurrent Autoregressive Networks for Online Multi-Object Tracking." arxiv preprint arxiv: (2017). Xiang, Yu, Alexandre Alahi, and Silvio Savarese. "Learning to track: Online multi-object tracking by decision making." 2015 IEEE international conference on computer vision (ICCV). No. EPFL-CONF IEEE, Sadeghian, Amir, Alexandre Alahi, and Silvio Savarese. "Tracking the untrackable: Learning to track multiple cues with long-term dependencies." arxiv preprint arxiv: (2017): 6. Cai, Zongwu, Jianqing Fan, and Qiwei Yao. "Functional-coefficient regression models for nonlinear time series." Journal of the American Statistical Association (2000): Xiong, Caiming, Stephen Merity, and Richard Socher. "Dynamic memory networks for visual and textual question answering." International Conference on Machine Learning Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "End-to-end memory networks." Advances in neural information processing systems Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems
32 Demo
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